1. Introduction
Graphs are a natural abstraction for representing relational data from multiple domains such as social networks, proteinprotein interactions networks, the World Wide Web, and so on. Analysis of such networks include classification (Bhagat et al., 2011), link prediction (LibenNowell and Kleinberg, 2007), detecting communities (Girvan and Newman, 2002; Blondel et al., 2008), and so on. Many of these tasks can be solved using machine learning algorithms. Unfortunately, since most machine learning algorithms require data to be represented as features, applying them to graphs is challenging due to their high dimensionality and structure. In this context, learning meaningful feature representation of graphs can help to leverage existing machine learning algorithms more widely on graph data.
Apart from classical dimensionality reduction techniques (see related work), recent works (Perozzi et al., 2014; Grover and Leskovec, 2016; Wang et al., 2016; Tang et al., 2015)
have explored various ways of learning feature representation of nodes in networks exploiting relationships to vector representations in NLP (like word2vec
(Mikolov et al., 2013)). However, application of such methods are limited to binary and muticlass node classification and edgeprediction. It is not clear how one can exploit these methods for other tasks like community detection which are inherently based on subgraphs and node embeddings result in loss of information of the subgraph structure. Embedding of subgraphs or neighborhoods themselves seem to be better suited for these tasks. Surprisingly, learning feature representation of networks themselves (subgraphs and graphs) has not gained much attention thus far. In this paper, we address this gap by studying the problem of learning distributed representation of subgraphs. Our contributions are:
We propose Sub2Vec, a scalable subgraph embedding method to learn features for arbitrary subgraphs that maintains the socalled local proximity.

We also provide theoretical justification of network embedding using Sub2Vec, based on language modeling tools. We also propose meaningful ways to measure how similar two subgraphs are to each other.

We conduct multiple experiments over large diverse real datasets to show correctness, scalability, and utility of features learnt by Sub2Vec in several tasks. In particular we get upto 4x better results in tasks such as community detection compared to just nodeembeddings.
(a) A network  (b) A set, , of subgraphs of  (c) embedding learned for each subgraph  (d) Intermediate neighborhoods 
on each subgraph 
The rest of the paper is organized as follows: we first formulate and motivate our problem, then present Sub2Vec, discuss experiments, and finally present related work, discussion and conclusions.
2. Problem Formulation
In this paper, we are interested in embedding subgraphs into a low dimensional continuous vector space. As shown later, the vector representation of subgraphs enables us to apply offtheshelf machine learning algorithms directly to solve subgraph mining tasks. For example, to group subgraphs together, we can apply clustering algorithms like KMeans directly. Figure 1 (ac) gives an illustration. Given a set of subgraphs (Figure 1 (b)) of a graph (Figure 1 (a)), we learn a lowdimensional feature representation of each subgraph (Figure 1(d)).
Now we are ready to formulate our Subgraph Embedding problem. We are given a graph where is the vertex set, and is the associated edgeset (we assume undirected graphs here, but our framework can be easily extended to directed graphs as well). We define as a subgraph of , where and . For simplicity, we write as . As input we require a set of subgraphs . Our goal is to embed subgraphs in into dimensional feature space , where . In addition, we want to ensure the subgraph proximity is wellpreserved in such a dimensional space. In this paper, we consider to preserve the “local neighborhood” of each subgraph . The idea is that if two subgraphs share common structure, then their vector representations in are close. We call such a measure Local Proximity.
Informal Definition 1 ().
(Local Proximity). Given two subgraphs and , the local proximity between and is larger if the commonly induced subgraph is larger.
Intuitively, local proximity measures how many nodes, edges, and paths are shared by two subgraphs. For illustration of the local proximity, let us consider an example. In Figure 2, suppose , , and are subgraphs induced by nodes and , and . Since, the subgraph commonly induced by and is larger than the subgraph commonly induced by and , we say and to be more “locally proximal” to each other than and . Note that the local proximity is not just the Jaccard similarity of nodes in the two subgraphs, as it also takes the connections among the common nodes into account.
Having defined the local proximity of two subgraphs, we focus on learning vector representations of subgraphs such that the local proximity is preserved. Formally, our Subgraph Embedding problem is,
Problem 1 ().
Given a graph , and set of subgraphs (of ) , learn an embedding function such that Local Proximity among subgraphs is preserved.
According to Problem 1, if and are closer to each other in terms of the local proximity that and then the has to be greater than , where is a similarity metric between two real vectors and in . Hence, if we embed the subgraphs in Figure 2 from the previous example, then a correct algorithm to solve Problem 1 has to ensure that . We propose an efficient algorithm for Problem 1 based on two different optimization objectives in the next section.
A natural question to ask is that if there are other metrics of subgraph similarity. Indeed, one can think of other measures of proximity, which may result in different embeddings. We will discuss this point further in Section 6.
3. Learning Feature Representations
In this section, we propose two optimization objectives for Problem 1
and propose an unsupervised deep learning technique to optimize the objectives.
Mikolov et al. proposed the continuous bag of words and skipgram models in (Mikolov et al., 2013), which have been extensively used in learning continuous feature representation of words. Building on these two models, Le et al. (Le and Mikolov, 2014) proposed two models: the Distributed Memory of Paragraph Vector (PVDM), and the Distributed Bag of Words version of Paragraph Vector (PVDBOW), which can learn continuous feature representations of paragraphs and documents.
Our main idea is to pose our feature learning problem as a maximum likelihood problem by extending PVDM and PVDBOW to networks. The direct analog is to treat each node as a word, and each subgraph as a paragraph. The edges within a subgraph can be thought as the adjacency relation of two words in a paragraph. PVDBOW and PVDM assume that if two paragraphs share similar sequence of words, they are close in the embedded feature space. The local proximity of subgraphs naturally follows the above assumption. Hence, we can leverage deep learning techniques in (Le and Mikolov, 2014)
for our subgraph embedding problem. PVDBOW and PVDM learn a latent representation by maximizing a distribution of word cooccurrences (using either ngram or skipgram model). Similarly, in this paper, we maximize a distribution of “node neighborhood”. The socalled “node neighborhood” is generated by subgraphtruncated random walks (see details in Section
3.3). We call our models Distributed Bag of Nodes version of Subgraph Vector (Sub2VecDBON) and Distributed Memory version of Subgraph Vector (Sub2VecDM) respectively.Next, we will introduce Sub2VecDM, Sub2VecDBON first, then study how to generate “node neighborhood” and give a justification from matrix multiplication view. Finally, we summarize our algorithm Sub2Vec.
3.1. Sub2VecDm
In the Sub2VecDM model, we seek to predict a node given other nodes in ’s neighborhoods and the subgraph belongs to. Consider the subgraph (a subgraph induced by nodes ) in Figure 2. Suppose the sequence of nodes returned by random walks in is , and we consider neighborhood of distance 2, then the model asks to predict node given subgraph , and its predecessors ( and ), i.e., .
More precisely, given a as the union graph of all the subgraphs in , where and , consider a function : (). We define as a node vector matrix, where each column is (the vector representation of nodes ). Similarly, we define function as the embedding function for subgraph , where is a dimensional vector. We denote as the subgraph matrix, where each column is for all subgraphs in . The matrices and are indexed by node and subgraph ids. In Sub2VecDM, we use the node and subgraph vectors to predict the next node in the neighborhood . We assume is given, and will discuss later in Section 3.3.
Now, given a node and its neighborhood and the subgraph from which the is drawn, the objective of Sub2VecDM is to maximize the following:
(1) 
where
is the probability of predicting node
in given the vector representations of its neighborhood and the subgraph from which the node and its neighborhood is drawn, . Note that for ease of description, we extend the function from a node to a node set (neighborhood ). is defined using the softmax function:3.2. Sub2VecDbon
In the Sub2VecDBON model, we want to predict the nodes in the subgraph given only the subgraph vector . For example, consider the same example in Section 3.1: the subgraph in Figure 2, and the node sequence generated by random walks. Now, in the Sub2VecDBON model the goal is to predict the neighborhood given the subgraph . This model is parallel to the popular skipgram model.
Formally, given a subgraph , and neighborhood drawn from , the objective of Sub2VecDBON is the following:
(3) 
where is also a softmax function, i.e.,
(4) 
Since computing Equation 4 involves summation over all possible neighborhoods, we use negative sampling to optimize it. The negative sampling objective is as follows:
(5) 
where is a parameter for negative sampling, is a context generated by random walks, and .
3.3. Subgraph Truncated Random Walks
Our problem seeks to preserve the local proximity between subgraph in . As mentioned in Section 2, intuitively the local proximity measures how many nodes, edges, and paths are shared by two subgraphs. However, quantify local proximity is challenging. A possible way to measure the local proximity between two subgraphs and , would be to look at their neighborhoods, and compare every neighborhood in with every neighborhood in . However, it is not feasible as we have a large number of neighborhoods. Another approach to measure local proximity is that we can enumerate all possible paths in each subgraphs. However, there are exponential number of paths in each subgraphs. To bypass these challenges, we resort to random walks to implement the local proximity.
Given a set of subgraphs , we generate neighborhood in each by fixed length subgraphtruncated random walks. Specifically, for a subgraph , we choose a node from nodes in uniformly at random. Next we generate a sequence of nodes to get a random walk of length , where is a node chosen from the neighbors of node uniformly at random. We repeat the process for each subgraph in . Overlaps in the random walks of and serve as a metric for local proximity. The intuition is that if the subgraph commonly induced by and is large, then we have more overlaps in their random walks.
Apart from being tractable in capturing the notion of local proximity between subgraphs, random walks have other advantages. First, the notion of neighborhood in other data types, such as texts, is naturally defined due to the sequential nature of text data. However, graphs are not sequential, hence it is more challenging to define the neighborhoods of subgraphs. Random walks help sequentialize subgraphs. Moreover, random walks generate meaningful sequences, for example, the frequency of nodes in random walk follows power law distribution (Perozzi et al., 2014).
3.4. Matrix Multiplication based Justification of our Model
Here we demonstrate that optimizing the objective function of SVDBON with negative sampling preserves the local proximity of subgraphs. Leveraging the idea in (Levy and Goldberg, 2014), we can write Equation 5 as a factorization of matrix , where each element corresponds to subgraph and context :
(6) 
is a negative sampling parameter, is a window size of context, and is a length of a random walk in each subgraph. Note that if subgraph in has contexts that is never observed, then in , . A common practice in NLP is to replace with where, if .
Suppose is the ath row in matrix , and is a dotproduct. Now, we have the following lemma.
Lemma 3.1 ().
Assuming random walks in subgraphs and visit every path of size at least once, then
(7) 
where is set of input subgraphs in the data, is the set of all the subgraphcontext pairs observed ,and is the number of overlapping paths of length in subgraphs and .
Proof.
Now, by the definition of dot product, we have the following:
(8) 
where is the number of times context appears in subgraph .
Now, we know that maximum value of is when random walk produces only context . And the minimum value of is , as the random walk visits each path in the subgraph if it exists. Now, summing only over nonzero entries.
(9) 
Now using the fact that for any and that there are exactly nonzero entries in the summation, we get
(10) 
∎
Lemma 3.1 shows that as the number of overlapping paths increases, the lower bound of any (corresponding to subgraphs and ) increases as well. Since optimizing ’s objective is closely related to the factorization of matrix , we can expect the embedding of subgraphs with higher overlaps to be closer to each other in the feature space. Hence, Sub2Vec preserves the local proximity.
3.5. Algorithm
In our algorithm, we first generate the neighborhood in each subgraph by running random walk. We then learn the vector representation of the subgraphs based on the random walks generated on each subgraph. Then stochastic gradient descent is used to optimize SVDBON/ SVDM objectives. The complete pseudocode is presented in Algorithms
1 and 2.4. Experiments
We briefly describe our setup next. All experiments are conducted using a 4 Xeon E74850 CPU with 512GB 1066Mhz RAM. We set the length of the random walk as 1000 and following literature (Grover and Leskovec, 2016), we set dimension of the embedding as 128 unless mentioned otherwise. The code was implemented in Python and we will release it for research purposes. We answer the following questions in our experiments:

[wide, labelwidth=!, labelindent=0pt, itemsep=0pt,nolistsep]

Are the embeddings learnt by Sub2Vec useful for community detection?

Are the embeddings learnt by Sub2Vec effective for link prediction?

How scalable is Sub2Vec for large networks?

Do parameter variations in Sub2Vec lead to overfitting?

Are the representations learnt by Sub2Vec meaningful?
Datasets. We run Sub2Vec on multiple real world datasets from multiple domains like socialinteractions, coauthorship, social networks and so on of varying sizes. See Table 1.

[wide, labelwidth=!, labelindent=0pt, itemsep=0pt,nolistsep]

WorkPlace is a publicly available social contact network between employees of a company with five departments^{1}^{1}1http://www.sociopatterns.org/. Edges indicate that two people were in proximity of each other.

HighSchool is a social contact network^{1}. Nodes are high school students belonging to one of five different sections and edges indicate that two students were in vicinity of each other.

Texas, Cornell, Washington, Wisconsin are networks from the WebKB dataset^{2}^{2}2http://linqs.cs.umd.edu/projects/projects/lbc/. These are networks of webpages and hyperlinks.

PolBlogs is a directed network of hyperlinks between weblogs on US politics, recorded in 2005.

AstroPH and DBLP are coauthorship networks from Arxiv Highenergy Physics and DBLP bibliographies respectively, where two authors have an edge if they have coauthored a paper.

Facebook (Leskovec and Mcauley, 2012) is an anonymized social network where nodes are Facebook users and edges indicate that two users are friends.

Youtube is a social network, where edges indicate friendship between two users.
Dataset  Domain  
WorkPlace (Genois et al., 2015)  92  757  contact 
Cornell (Sen et al., 2008)  195  304  web 
HighSchool (Fournet and Barrat, 2014)  182  2221  contact 
Texas (Sen et al., 2008)  187  328  web 
Washington (Sen et al., 2008)  230  446  web 
Wisconsin (Sen et al., 2008)  265  530  web 
PolBlogs (Adamic and Glance, 2005)  1490  16783  web 
Facebook (Leskovec and Mcauley, 2012)  4039  88234  socialnetwork 
AstroPH (Leskovec et al., 2007)  18722  199110  coauthor 
DBLP (Yang and Leskovec, 2015)  317k  1.04 M  coauthor 
Youtube (Yang and Leskovec, 2015)  1.13M  2.97M  social 
4.1. Community Detection
Setup. Here we show how to leverage Sub2Vec for the wellknown community detection problem. A community of nodes in a network is a coherent group of nodes which are roughly densely connected among themselves and sparsely connected with the rest of the network. As nodes in a community are densely connected to each other, we expect neighboring nodes in the same community to have a similar surrounding. We know that Sub2Vec embeds subgraphs while preserving local proximity. Therefore, intuitively we can use features generated by Sub2Vec to detect communities.
Specifically, we propose to solve the community detection problem using Sub2Vec by embedding the surrounding neighborhood of each node. First, we extract the neighborhood of each node from the input graph . Then we run Sub2Vec on to learn feature representation of for all
. We then use a simple clustering algorithm (KMeans) to cluster the feature vectors
of all egonets. Cluster membership of egonets determines the community membership of the ego. The complete pseudocode is in Algorithm 3.In Algorithm 3, we define neighborhood of each node to be its egonetwork for dense networks (HighSchool and WorkPlace) and 2hop egonetworks for sparse networks. The egonetwork of a node is the subgraph induced by the node and its neighbors. Similarly, the 2hop egonetwork of a node is defined as the subgraph induced by the node, its neighbors, and neighbors’ neighbors.
We compare Sub2Vec with various traditional community detection algorithms and network embedding based methods. Newman (Girvan and Newman, 2002)
is a community detection algorithm based on betweenness. It is a greedy agglomerative hierarchical clustering algorithm.
Louvian (Blondel et al., 2008) is a greedy optimization method. Node2Vec is a network embedding method which learns feature representation of nodes in the network which we then cluster to obtain communities.We run Sub2Vec and baselines on the following networks with ground truth communities and compute Precision, Recall, and F1 score to evaluate all the methods.

[wide, labelwidth=!, labelindent=0pt, itemsep=0pt]

WorkPlace: Each department as a ground truth community.

HighSchool: Each section as a ground truth community.

Texas, Cornell, Washington: Each webpage belongs to one of five classes: course, faculty, student, project, and staff, which serve as groundtruth.

PolBlogs: Conservative and liberal blogs as groundtruth communities.
WorkPlace  HighSchool  PolBlogs  Texas  Cornell  Washington  Wisconsin  

Method  P  R  F1  P  R  F1  P  R  F1  P  R  F1  P  R  F1  P  R  F1  P  R  F1 
Newman  0.26  0.27  0.27  0.23  0.32  0.27  0.67  0.64  0.66  0.43  0.15  0.22  0.38  0.25  0.30  0.32  0.87  0.47  0.35  0.13  0.19 
Louvian  0.57  0.04  0.07  0.49  0.04  0.08  0.91  0.83  0.87  0.54  0.14  0.23  0.36  0.15  0.22  0.45  0.1  0.16  0.40  0.12  0.19 
Node2Vec  0.26  0.21  0.23  0.21  0.22  0.22  0.92  0.92  0.92  0.41  0.63  0.50  0.30  0.36  0.33  0.37  0.45  0.40  0.34  0.24  0.29 
Sub2Vec DM  0.87  0.69  0.77  0.95  0.95  0.95  0.92  0.93  0.93  0.49  0.57  0.53  0.34  0.47  0.39  0.45  0.64  0.53  0.40  0.42  0.41 
Sub2Vec DBON  0.86  0.67  0.77  0.94  0.94  0.94  0.92  0.92  0.92  0.44  0.59  0.51  0.31  0.55  0.40  0.43  0.66  0.52  0.35  0.41  0.38 
Results. See Table 2. Both versions of Sub2Vec significantly and consistently outperform all the baselines (upto a factor of 4 times against closest competitor, Node2Vec). We do better than Node2Vec because intuitively, we learn the feature vector of the neighborhood of each node for the community detection task; while Node2Vec just does random probes of the neighborhood. Precision for Louvian is high in dense networks as it outputs small communities and recall is consistently poor across all datasets for the same reason, while for Newman the performance is not consistent. Performance of Node2Vec is satisfactory in the sparse networks like PolBlogs and Texas, but it is significantly worse for dense networks like WorkPlace and HighSchool. On the other hand, performance of Sub2Vec is even more impressive in these networks.
In Figure 3, we plot the community structure of the HighSchool dataset. In the HighSchool dataset, we consider five sections as the ground truth community. In the figure, the color of nodes indicate the community membership. The figure highlights the superiority of Sub2Vec compared to Node2Vec. The communities discovered by Sub2Vec matches the ground truth very closely, while those discovered by Node2Vec appear to be near random.
(a) Ground Truth  (b) Result of node2vec  (c) Result of Sub2Vec 
4.2. Link Prediction
Setup. In this section, we focus on the Link Prediction problem. Given a network , the link prediction problem asks to predict the likelihood of formation of an edge between two nodes and , such that . It is well known that nodes with common neighbors tend to form future links (LibenNowell and Kleinberg, 2007). For example, in a social network two individuals who have multiple friends in common have higher chances of eventually forming a friendship. It is evident from the example that likelihood of future edges depends on the similarity of neighborhood around each endpoint. Hence we propose exploiting the embeddings of egonets of each node obtained from Sub2Vec to predict whether two nodes will form an edge.
Specifically, we first hide a percentage of edges randomly sampled from the network, while ensuring that the remaining network remains connected. We consider these “hidden” edges as the ground truth. Then we extract the egonetwork, , for each node . We then run Sub2Vec on and use the resulting embedding to predict link. Following methodology in literature (Wang et al., 2016), to evaluate our method, we calculate the Mean Average Precision (MAP). To calculate MAP first we compute Precision@K, as . Here is the node predicted to have edge with node and if is in the ground truth, otherwise. Then we compute the Average Precision as . Finally, MAP is given as:
We compare our result with Node2Vec only as it was previously shown to be better than other baselines (Grover and Leskovec, 2016).
Results. See Table 3. Firstly, note that Sub2Vec outperforms Node2Vec as varies from 10 to 30 in all the datasets. We also notice that Sub2Vec DM performs surprisingly worse than Node2Vec and Sub2Vec DBON on Facebook. The reason for its poor performance in Facebook is that the network is dense with average clustering coefficient of 0.6 and effective radius of 4 for 90% of the nodes. Recall that the Sub2Vec DM optimization relies on finding the embedding of the nodes as well, which will not be discriminative for dense networks. In contrast, Sub2Vec DBON learns the features of subgraps directly, without relying on node embeddings, and hence it performs very well on large dense networks including Facebook. Finally we see that Node2Vec consistently improves as increases, while both versions of Sub2Vec either deteriorate or stagnate. We discuss this more in Section 6.
WorkPlace  HighSchool  AstroPH  
Node2Vec  S DBON  S DM  Node2Vec  S DBON  S DM  Node2Vec  S DBON  S DM  Node2Vec  S DBON  S DM  
10  0.25  0.37  0.33  0.39  0.42  0.52  0.50  0.77  0.29  0.12  0.24  0.31 
20  0.36  0.28  0.42  0.41  0.52  0.26  0.68  0.84  0.34  0.21  0.31  0.28 
30  0.39  0.28  0.40  0.50  0.45  0.57  0.72  0.83  0.35  0.26  0.37  0.44 
4.3. Parameter Sensitivity
(a) Walk length  (b) Dimension of Vectors 
Here we discuss the parameter sensitivity of Sub2Vec. We show how the F1 score for community detection task on PolBlogs dataset changes when we change the two parameters of Sub2Vec: (i) length of the random walk and (ii) dimension of the embedding. As shown in Figure 4 (a), the F1 score is 0.85 even when we do random walks of length 500. For the higher length, the F1 score remains constant.
Similarly, to see how the results of the community detection task changes with the size of the embedding, we run the community detection task on PolBlogs with varying embedding dimension. See Figure 4 (b). The F1 score saturates when the dimension of vector is greater than 100.
4.4. Scalability
(a) No of Subgraphs  (b) Size of Subgraphs 
Here we show the scalability of Sub2Vec with respect to the number and the size of subgraphs. We extract connected subgraphs of Youtube dataset of induced by varying percentage of nodes. We then run Sub2Vec on the set of egonets in each resulting network. As shown in Figure 5 (a), Sub2Vec is linear w.r.t number of subgraphs. In Figure 5 (b), we run Sub2Vec on 1 to 3 hops egonets of AstroPH dataset. We see a significant jump in the running time when the hop increases from 2 to 3. This is due to the fact that as the hop of egonet increases, the size of the subgraph increases exponentially due to the low diameter of real world networks.
4.5. Case Studies
We perform casestudies on MemeTracker ^{3}^{3}3snap.stanford.edu and DBLP to investigate if our embeddings are interpretable. MemeTracker consists of a series of cascades caused by memes spreading on the network of linked web pages. Each memecascade induces a subgraph in the underlying network. We first embed these subgraphs in a continuous vector space by leveraging Sub2Vec. We then cluster these vectors to explore what kind of meme cascadegraphs are grouped together, what characteristics of memes determine their similarity and distance to each other and so on. For this casestudy, we pick the top 1000 memes by volume in the data. And we cluster them into 10 clusters using KMeans.
We find coherent clusters which are meaningful groupings of memes based on topics. For example we find cluster of memes related to different topics such as entertainment, politics, religion, technology and so on. Visualization of these clusters is presented in Figure 6. In the entertainment cluster, we find memes which are names of popular songs and movies such as “sweet home alabama”,“somewhere over the rainbow”, “Madagascar 2” and so on. Similarly, we also find a cluster of religious memes. These memes are quotes from the Bible. We also find memes related to politics and religion in the same cluster such as “separation of church and state”’. In politics cluster, we find popular quotes from the 2008 presidential election season e.g. Barack Obama’s popular slogan “yes we can” along with his controversial quotes like “you can put lipstick on a pig” in the cluster. We also find Sarah Palin’s quote like “the chant is drill baby drill”. Similarly, we also find a cluster of technology/video games related memes.
Interestingly, we find that all the memes in Spanish language were clustered together. This indicates that memes in different language travel though separate websites, which matches with the reality as most webpages use one primary language. We also noticed that some of the clusters did not belong to any particular topic. Upon closer examination we found out that these clusters contained memes which were covered by general news website such as msnbc.com, yahoo.com, news.google.com and local news websites such as philly.com from Philadelphia and breakingnews.ie from Ireland.
(a) Politics Cluster  (b) Religion Cluster  (c) Spanish Cluster 
(d) Entertainment Cluster  (e) Technology Cluster 
For DBLP, we follow the methodology in (Lappas et al., 2010), and extract subgraphs of the coauthorship network based on the keywords contained in the title of the papers.
We include keywords such as ‘classification’, ‘clustering’, ‘xml’, and so on. Once we extract the subgraphs, we run Sub2Vec to learn embedding of these subgraphs. We then project the embeddings down to 2dimensions using tSNE (Maaten and Hinton, 2008).
See Figure 7. We see some meaningful groupings in the plot. We see that the keyword related to each other such as ‘graphs’, ‘pagerank’, ‘crawling’, and ‘clustering’ appear together. The classification related keywords such as ‘boosting’, ‘svm’, and ‘classification’ are grouped together. We also see that ‘streams’ and ‘wavelets’ are close to each other. These meaningful groups of keywords highlight the fact that Sub2Vec results in meaningful embeddings.
5. Related Work
Network Embedding. The network embedding problem has been well studied. Most of work seeks to generate low dimensional feature representation of nodes. Early work includes Laplacian Eigenmap (Belkin and Niyogi, 2001), IsoMap (Tenenbaum et al., 2000), locally linear embedding (Roweis and Saul, 2000), and spectral techniques (Bach and Jordan, 2003; Chung, 1997). Recently, several deep learning based network embeddings algorithms were proposed to learn feature representations of nodes (Perozzi et al., 2014; Wang et al., 2016; Tang et al., 2015; Grover and Leskovec, 2016). Perozzi et. al (Perozzi et al., 2014) proposed DeepWalk, which extends skipGram model (Mikolov et al., 2013) to networks and learns feature representation based on contexts generated by random walks. Grover et. al. proposed a more general method, Node2Vec (Grover and Leskovec, 2016), which generalizes random walks to generate various contexts. SDNE (Wang et al., 2016) and LINE (Tang et al., 2015) learn feature representation of nodes while preserving first and second order proximity. However, all of them learn low dimensional feature vector of nodes, while our goal is to embed subgraphs.
The most similar network embedding literature includes (Riesen and Bunke, 2010; Yanardag and Vishwanathan, 2015; Narayanan et al., 2016). Risen and Bunke propose to learn vector representations of graphs based on edit distance to a set of predefined prototype graphs (Riesen and Bunke, 2010). Yanardag et. al. (Yanardag and Vishwanathan, 2015) and Narayanan et al. (Narayanan et al., 2016) learn vector representation of the subgraphs using the Word2Vec (Mikolov et al., 2013) by generating ”corpus” of subgraphs where each subgraph is treated as a word. The above work focuses on some specific subgraphs like graphlets and rooted subgraphs. None of them embed subgraphs with arbitrary structure. In addition, we interpret subgraphs as paragraphs, and leverage the PVDBOW and PVDM model (Le and Mikolov, 2014).
Other Subgraph Problems. There has been a lot of work on subgraph related problems. For example, the subgraph discovery problems have been studies extensively. Finding the largest clique is a wellknown NPcomplete problem (Karp, 1972), which is also hard to approximate (Hstad, 1996). Lee et al. surveyed dense subgraph discovery algorithms for several subgraphs including clique, Kcore, Kclub, etc (Lee et al., 2010). Akoglu et al. extended the subgraph discovery problem to attributed graphs (Akoglu et al., 2012)
. Perozzi et al. studied the attributed graph anomaly detection by exploring the neighborhood subgraph of a nodes
(Perozzi and Akoglu, 2016). Different from the above works, we seek to find feature representations of subgraphs.6. Discussion
WorkPlace  HighSchool  AstroPH  

Node2Vec  S DBON  S DM  Node2Vec  S DBON  S DM  Node2Vec  S DBON  S DM  Node2Vec  S DBON  S DM  
40  0.45  0.32  0.35  0.60  0.47  0.56  0.75  0.78  0.22  0.30  0.39  0.33 
50  0.48  0.31  0.33  0.57  0.42  0.49  0.78  0.75  0.12  0.33  0.26  0.34 
60  0.50  0.33  0.32  0.60  0.40  0.43  0.79  0.53  0.1  0.34  0.29  0.29 
We have shown that Sub2Vec gives meaningful interpretable embeddings of arbitrary subgraphs. We have also shown via our experiments that Sub2Vec outperforms traditional algorithms as well as nodelevel embedding algorithms for extracting communities from networks, especially in challenging dense graphs. Similarly for link prediction, we also showed that embedding neighborhoods is better for finding correct links.
So for which tasks will Sub2Vec not be ideal? For link prediction, as previously mentioned in Section 4, the performance of Sub2Vec deteriorates when higher percentages of edges are removed from the network. The results for higher percentages, = 40 to 60, is presented in Table 4. The result shows that Node2Vec outperforms Sub2Vec in such cases, despite performing poorly for lower values of . This happens because, as increases, the density of the network decreases and results in lesser overlaps in the neighborhoods of nearby nodes. Hence Sub2Vec which preserves the local proximity of subgraphs, does not embed such subgraphs very close to each other, resulting in poorer prediction performance.
We believe, in such situations, perhaps using other proximity measures between subgraphs is more meaningful to preserve during the embedding process than only local proximity.
One such way can be using ‘positional promixity’, where two subgraphs are proximal based on their position in the network. For example, in Figure 2, subgraphs induced by nodes and are similar to each other as the member nodes in these two subgraphs have similar roles. Nodes and both connect to central node and nodes and both have degree two. Using just local proximity, these subgraphs are not similar.
Positional Proximity: If we are given two subgraphs and , then the positional proximity between and is determined by similarity of position of nodes in and .
Similarly, another way can be using similarity based on structure of subgraphs. For example, in Figure 2, subgraphs induced by nodes and are similar to each other as both of them are cliques of size four.
Structural Proximity: If we are given two subgraphs and , then the structural proximity between and is determined by the structural properties of and .
For link prediction in very sparse networks, Positional Proximity might give more useful embeddings than Local Proximity. We leave the task of embedding subgraphs based on Structural and Positional proximities (or using a combination with Local proximity) and leveraging them for graph mining as future work.
7. Conclusion
We have presented Sub2Vec, a scalable feature learning framework for a set of subgraphs such that the local proximity between them are preserved. In contrast most prior work focused on finding nodelevel embeddings. We give a theoretical justification and showed that the embeddings generated by Sub2Vec can be leveraged in downstream applications such as community detection and link prediction. We also performed casestudies on two real networks to validate the usefulness of the subgraph features generated by Sub2Vec.
References
 (1)
 Adamic and Glance (2005) Lada A Adamic and Natalie Glance. 2005. The political blogosphere and the 2004 US election: divided they blog. In Proceedings of the 3rd international workshop on Link discovery. ACM, 36–43.
 Akoglu et al. (2012) Leman Akoglu, Hanghang Tong, Brendan Meeder, and Christos Faloutsos. 2012. PICS: Parameterfree identification of cohesive subgroups in large attributed graphs. In Proceedings of the 2012 SIAM international conference on data mining. SIAM, 439–450.

Bach and Jordan (2003)
Francis R Bach and
Michael I Jordan. 2003.
Learning spectral clustering. In
NIPS, Vol. 16.  Belkin and Niyogi (2001) Mikhail Belkin and Partha Niyogi. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS, Vol. 14. 585–591.
 Bhagat et al. (2011) Smriti Bhagat, Graham Cormode, and S Muthukrishnan. 2011. Node classification in social networks. In Social network data analytics. Springer, 115–148.
 Blondel et al. (2008) Vincent D Blondel, JeanLoup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment 2008, 10 (2008), P10008.
 Chung (1997) Fan RK Chung. 1997. Spectral graph theory. Vol. 92. American Mathematical Soc.
 Fournet and Barrat (2014) Julie Fournet and Alain Barrat. 2014. Contact Patterns among High School Students. PLoS ONE 9, 9 (09 2014), e107878.
 Genois et al. (2015) Mathieu Genois, Christian Vestergaard, Julie Fournet, Andre Panisson, Isabelle Bonmarin, and Alain Barrat. 2015. Data on facetoface contacts in an office building suggest a lowcost vaccination strategy based on community linkers. Network Science 3 (9 2015), 326–347. Issue 03.
 Girvan and Newman (2002) Michelle Girvan and Mark EJ Newman. 2002. Community structure in social and biological networks. Proceedings of the national academy of sciences 99, 12 (2002), 7821–7826.
 Grover and Leskovec (2016) Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 855–864.
 Hstad (1996) Johan Hstad. 1996. Clique is hard to approximate within n1. In Proc. 37th Symp. on Found. Comput. Sci. 627–636.
 Karp (1972) Richard M Karp. 1972. Reducibility among combinatorial problems. In Complexity of computer computations. Springer, 85–103.
 Lappas et al. (2010) Theodoros Lappas, Evimaria Terzi, Dimitrios Gunopulos, and Heikki Mannila. 2010. Finding effectors in social networks. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1059–1068.
 Le and Mikolov (2014) Quoc V Le and Tomas Mikolov. 2014. Distributed Representations of Sentences and Documents.. In ICML, Vol. 14. 1188–1196.
 Lee et al. (2010) Victor E Lee, Ning Ruan, Ruoming Jin, and Charu Aggarwal. 2010. A survey of algorithms for dense subgraph discovery. In Managing and Mining Graph Data. Springer, 303–336.
 Leskovec et al. (2007) Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. 2007. Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data (TKDD) 1, 1 (2007), 2.
 Leskovec and Mcauley (2012) Jure Leskovec and Julian J Mcauley. 2012. Learning to discover social circles in ego networks. In Advances in neural information processing systems. 539–547.
 Levy and Goldberg (2014) Omer Levy and Yoav Goldberg. 2014. Neural word embedding as implicit matrix factorization. In Advances in neural information processing systems. 2177–2185.
 LibenNowell and Kleinberg (2007) David LibenNowell and Jon Kleinberg. 2007. The linkprediction problem for social networks. journal of the Association for Information Science and Technology 58, 7 (2007), 1019–1031.
 Maaten and Hinton (2008) Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using tSNE. Journal of Machine Learning Research 9, Nov (2008), 2579–2605.
 Mikolov et al. (2013) Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS. 3111–3119.
 Narayanan et al. (2016) Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan. 2016. subgraph2vec: Learning distributed representations of rooted subgraphs from large graphs. arXiv preprint arXiv:1606.08928 (2016).
 Perozzi and Akoglu (2016) Bryan Perozzi and Leman Akoglu. 2016. Scalable anomaly ranking of attributed neighborhoods. In Proceedings of the 2016 SIAM International Conference on Data Mining. SIAM, 207–215.
 Perozzi et al. (2014) Bryan Perozzi, Rami AlRfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 701–710.
 Riesen and Bunke (2010) Kaspar Riesen and Horst Bunke. 2010. Graph classification and clustering based on vector space embedding. World Scientific Publishing Co., Inc.
 Roweis and Saul (2000) Sam T Roweis and Lawrence K Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. science 290, 5500 (2000), 2323–2326.
 Sen et al. (2008) Prithviraj Sen, Galileo Mark Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina EliassiRad. 2008. Collective Classification in Network Data. AI Magazine 29, 3 (2008), 93–106.
 Tang et al. (2015) Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Largescale information network embedding. In Proceedings of the 24th International Conference on World Wide Web. ACM, 1067–1077.
 Tenenbaum et al. (2000) Joshua B Tenenbaum, Vin De Silva, and John C Langford. 2000. A global geometric framework for nonlinear dimensionality reduction. science 290, 5500 (2000), 2319–2323.
 Wang et al. (2016) Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1225–1234.
 Yanardag and Vishwanathan (2015) Pinar Yanardag and SVN Vishwanathan. 2015. Deep graph kernels. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1365–1374.
 Yang and Leskovec (2015) Jaewon Yang and Jure Leskovec. 2015. Defining and evaluating network communities based on groundtruth. Knowledge and Information Systems 42, 1 (2015), 181–213.
Comments
There are no comments yet.