I Introduction
Many information retrieval and mining tasks such as node classification [7], clustering [30], link prediction [28], and information diffusion [25]
become timeconsuming in largescale networks. This motivates researchers to develop network embedding techniques which aim to learn a distributed representation vector for each node in a network. An effective network embedding should preserve the similarity between nodes in order to reconstruct the original network.
The word2vec [21] idea has inspired many studies for network representation learning, most of which are in the context of homogeneous information networks, such as DeepWalk [22], LINE [31], and node2vec [9]. A homogeneous information network is a simple structural network, where all nodes and links are considered to belong to a single class.
However, in practice, there are usually multiple types of nodes (e.g., authors and papers in DBLP) and links (e.g., cite and publish) that compose a heterogeneous information network (HIN). To measure the similarity between nodes in HINs, many customized similarity or relevance measures based on metapaths have been proposed in recent years [16, 29]. For example, a metapath (denoted as ) indicates two authors having their publications in the same venue. Comparing to metapathbased relevance measures utilizing only simple structural information, metagraph [15] is recently proposed to capture complex structural information in HINs. In short, metagraph is a special directed acyclic graph (DAG) which contains at least two embedded metapaths, such as a DAG containing and as shown in Figure 1, where is the topic of a paper.
Metagraph is an effective tool to calculate the relevance score between nodes in HINs, where a higher score indicates that there are more metagraph instances between two nodes, i.e., a closer relationship. How to explore metagraphs for representation learning in HINs is still an open question. An intuitive idea for metagraphbased representation learning is to learn the node embedding by leveraging multiple metagraphs between nodes in HINs. However, existing metagraphbased relevance measures only utilize the strong relations as defined by the metagraphs themselves, and they usually ignore the weak relations as indicated by their embedded metapaths. To address this problem, we propose to learn the node embedding by leveraging both metagraph and its embedded metapaths for similarity search. An effective representation learning based on a single metagraph should contain both strong and weak relations embedded in this metagraph. In addition, we explore a novel metagraphbased similarity measure to compute relevance scores that can better capture the strong relations between nodes in HINs.
In summary, there are threefold contributions of this paper: 1) We are the first to propose the metagraphbased node embedding method in HINs. Specifically, we develop two kinds of node embedding methods based on metagraph, named MEGA and MEGA++ respectively. 2) We introduce GraphSim which is an effective metagraphbased similarity measure with best performance comparing to previous metagraphbased similarity measures, such as StructCount and SCSE. 3) Our approaches show the best performance comparing to other competing methods on two realworld datasets.
Ii Preliminary and Problem Formulation
In this section, we first introduce some related concepts and notations from multilinear algebra. Then, we review some concepts and approaches involved in HIN analysis including metagraph and relevance measure. Last part, we formulate the problem of node embedding in HINs.
Iia Multilinear Algebra
The basic mathematical object of multilinear algebra is the tensor, a higher order generalization of vectors (first order tensors) and matrices (second order tensors) to multiple indices. The order of a tensor is the number of dimensions, also known as modes or ways. An th order tensor is represented as , where is the cardinality of its th mode, . An element of a vector , a matrix , or a tensor is denoted by , , , etc., depending on the number of modes. All vectors are column vectors unless otherwise specified. For an arbitrary matrix , its th row and th column vector are denoted by and , respectively.
Definitions of outer product, partial symmetric tensor, mode matricization, and CP factorization are given below, which will be applied to present our approach.
Definition 1
(Outer Product) The outer product of vectors for is an th order tensor and defined elementwise by for all values of the indices.
Definition 2
(Partial Symmetric Tensor) An th order tensor is a rankone partial symmetric tensor if it is partial symmetric on modes , and can be written as the tensor product of vectors, i.e.,
(1) 
where .
Definition 3
(Mode Matricization) The mode matricization or unfolding of an th order tensor is denoted by and is of size , where .
Definition 4
(CP Factorization) For a general tensor , its CANDECOMP/PARAFAC (CP) factorization is
(2) 
where for , are factor matrices of size , is the number of factors, and is used for shorthand.
To obtain the CP factorization
, the objective is to minimize the following estimation error:
(3) 
However, is not jointly convex w.r.t. . A widely used optimization technique is the Alternating Least Squares (ALS) algorithm, which alternatively minimize for each variable while fixing the other, that is,
(4) 
where .
IiB Meta Graph
Definition 5
(MetaGraph [15]) A metagraph is a directed acyclic graph (DAG) defined on a HIN schema . A metagraph contains a single source node with 0 in degree and a single target node with 0 out degree. Mathematically, a metagraph , where is a set of nodes, is a set of edges, is the of source node, and is the target node,.
Since a metagraph only has one source node and one target node, not all subgraphs of HINs can be metagraph.
Definition 6
(Metagraphbased Relevance Measure) Given a HIN and a metagraph , the similarity of any two nodes with respect to is defined as:
(5) 
where is a metagraph instance of , and is the relevance score between and , which will be determined by the number of metagraph instances connecting them.
Prior works provide different metagraphbased relevance measures, such as StructCount, SCSE and BSCSE [15].
IiC Problem Formulation
We study the problem of metagraphbased node embedding in the HIN. Given a HIN , we have two goals in this study. First, we want to explore a customized metagraphbased relevance measure which can more efficiently capture the complex structural information. Second, we aim at finding an effective node embedding that can better preserve the closeness between nodes in a HIN based on a metagraph and its embeded metapaths analysis. Specifically, we integrate all the similarity information of a metagraph and its embedded metapaths into a symmetric matrix and a partial symmetric tensor, and perform multilinear analysis of the coupled partial symmetric tensor and symmetric matrix to find the node embedding.
Iii Methods
In this section, we will introduce a brand new similarity measure, and the embedding techniques of MEGA++. First, we will introduce a metagraphbased similarity measure named GraphSim. Then, we proposed a coupled tensormatrix decomposition to obtain a joint embedding for nodes in HINs.
Iiia GraphSim: A Normalized version of StructCount
First, we want to propose a new metagraphbased similarity measure called GraphSim. In previous work, Huang et al. [15] proposed three metagraphbased similarity measures: StructCount, SCSE, and BSCSE which is a mixed measure based on previous two measures. GraphSim can be viewed as a normalized version of StructCount.
StructCount [15] is a straightforward metagraphbased similarity measure in HIN, which counts the number of metagraph instances in the graph with an as source and an as target object.
Definition 7
(GraphSim) A metagraphbased similarity measure. Given a symmetric metagraph , GraphSim between two nodes is defined as:
(6) 
where is a metagraph instance between and , is that between and , and is that between and .
Comparing to StructCount, GraphSim is normalized version of StructCount. is determined by two parts: First, the number of metagraph instance between by following ; Second, the balance of their visibility, where the visibility is defined as the number of metagraph instances between themselves. Normalized relevance score can present better relation between different nodes. For example, an author published all his four papers with . published five papers with , and published other five papers with other authors. Without normalized process, the relation between and is closer than and . However, for common sense, we should agree and have closer relation, which indicates GraphSim is a better measure.
Comparing to three measures in [15],
of GraphSim is between 0 and 1 like SCSE. However, SCSE measures the random walk probability that
expands a metagraph instance to . In our application, we find GraphSim shows better performance than all those three metagraph measures [15].IiiB Mega++: Node Embedding by CTMD
In this section, we show how to jointly consider similarity information of a metagraph and its embedded metapaths to learn a node embedding. The basic idea is the integration of similarity matrices and coupled embedding by joint factorization. Specifically, we first compute a metagraph similarity matrix using the proposed GraphSim, denoted as , and for each metapath , compute an embedded metapath similarity matrix using the PathSim, denoted as . Next, we concatenate the embedded metapath similarity matrices of different embedded metapaths to form a thirdorder tensor comprising three modes: nodes, nodes, and paths, denoted as . Then, we introduce a novel coupled tensormatrix decomposition (CTMD) method to find common latent features between and . Last, we use the latent features to measure the similarity between different nodes in the HIN.
In the following, we detail the CTMD method, which can be seen as a special case of the coupled tensormatrix decomposition [1] with input partial symmetric tensor and symmetric matrix . Notice that since similarity matrix is symmetric, the resulting is a partial symmetric tensor, and is a symmetric matrix.
Tensors (including matrix) provide a natural and efficient representation for a metagraph data, but there is no guarantee that such representation will be good for subsequent learning, since learning will only be successful if the regularities that underlie the data can be discerned by the model. Tensor factorization is a powerful tool to analyze tensors. In previous work, it was found that CP factorization (which is a higher order generalization of SVD) is particularly effective to acknowledge the connections and find valuable features among tensor data [32]. Motivated by these observations, we exploit the benefits of CP and SVD factorizations to find an effective embedding in the sense of metapathbased similarity tensor and metagraph similarity matrix .
Based on above analysis, we design our CTMD objective function as below:
(7) 
where and are latent matrices. Specifically, is jointly learned from both metagraph and metapath similarity information.
The objective function in Eq. (7) is nonconvex with respect to and together, thus there is no closedform solution. We introduce an effective iteration method to solve this problem. The main idea is to decouple the parameters using an Alternating Direction Method of Multipliers (ADMM) approach [4], by alternatively optimizing the objective with respect to one variable, while fixing others.
Update : First, we optimize while fixing . Notice that the objective function in Eq. (7) involves a fourthorder term with respect to which is difficult to optimize directly. To obviate this problem, we use a variable substitution technique and minimize the following objective function
(8) 
where is an auxiliary variable.
The augmented Lagrangian function of Eq. (8) is
(9) 
where is the Lagrange multiplier, and is the penalty parameter which can be adjusted efficiently according to [18].
By setting the derivative of Eq. (10) with respect to to zero, we obtain the closedform solution
(11) 
To efficiently compute , we consider the following property of the KhatriRao product of two matrices
(12) 
Then the auxiliary matrix can be optimized successively in a similar way, and the solution is
(13) 
where is the mode2 matricization of , and .
Moreover, we optimize the Lagrange multiplier using the gradient descent method by
(14) 
Update : Next, we optimize while fixing and . We need to optimize the following objective function
(15) 
where is the mode3 matricization of , and .
By setting the derivative of Eq. (15) with respect to to zero, we obtain the closedform solution as
(16) 
The overall algorithm is summarized in Algorithm 1.
IiiC Time Complexity
Each iteration in Algorithm 1 consists of simple matrix operations. Therefore, rough estimates of its computational complexity can be easily derived based on ADMM [17].
The estimate for the update of according to Eq. (11) is as follows: for the computation of the term ; for the computation of the term due to Eq. (12) and for its Cholesky decomposition; for the computation of the system solution that gives the updated value of . An analogous estimate can be derived for the update of .
Overall, the updates of model parameters and , require O() arithmetic operations in total.
Iv Experiments
In this section, we conduct extensive experiments in order to test the effectiveness of the proposed methods: GraphSim, MEGA and MEGA++. We first introduce two reallife datasets and a set of methods to be compared. Then, we evaluate the effectiveness of proposed methods on four data mining tasks: clustering, classification, parameter analysis and time analysis.
We use two real datasets (e.g. DBLP4Area and YAGO Movie) in the evaluation. Table I shows some statistics about them. DBLP4Area [29] is the subset of original DBLP, which contains 5,237 papers (P), 5,915 authors (A), 18 venues (V), 4,479 topics (T). The authors and venues are from 4 areas: database, data mining, machine learning and information retrieval. YAGO Movie is a subset of YAGO [15], which contains 7,332 movies (M), 10,789 actors (A), 1,741 directors (D), 3,392 producers (P) and 1,483 composers (C). The movies are divided into five genres: action, horror, adventure, scifi and crime. The guided metagraphs are designed for three tasks as shown in the Figures 1 and 3.
The proposed methods are compared with metagraphbased relevance measures (e.g. StructCount, SCSE, and BSCSE [15]), and network embedding approaches (e.g. DeepWalk [22], and LINE [31]) in clustering and classification tasks. The experimental results are shown in the following sections.
Avg. degree  

DBLP  15,649  51,377  6.57  4  4 
MOVIE  25,643  40,173  3.13  5  4 
Pre. MetaGraph Measures  Pre. Network Embedding  OUR WROKS  
Task  Method  StuctCount  SCSE  BSCSE()  LINE  DeepWalk  GraphSim  MEGA  MEGA++ 
DBLP (V2V)  NMI  0.2634  0.6309  0.6309  0.7954  0.8258  0.8479  0.8521  0.8718 
Purity  0.5000  0.7333  0.7333  0.8042  0.8584  0.8744  0.8817  0.8956  
DBLP (A2A)  NMI  0.0338  0.0156  0.0156  0.3920  0.4896  0.2150  0.5263  0.5315 
Purity  0.2997  0.2822  0.2823  0.7135  0.7941  0.4903  0.7956  0.7989  
Moive (M2M)  NMI  0.0011  0.0008  0.0008  0.0008  0.0007  0.0021  0.0045  0.0045 
Purity  0.2991  0.2988  0.2988  0.2981  0.2981  0.3002  0.3017  0.3032  
Overall  NMI  0.0994  0.2158  0.2158  0.3961  0.4387  0.3550  0.4610  0.4693 
Purity  0.3663  0.4381  0.4381  0.6052  0.6502  0.5550  0.6597  0.6659 
Pre. MetaGraph Measures  Pre. Network Embedding  OUR WROKS  
Task  Method  StuctCount  SCSE  BSCSE()  LINE  DeepWalk  GraphSim  MEGA  MEGA++ 
DBLP (A2A)  MacroF1  0.734  0.616  0.734  0.816  0.839  0.818  0.863  0.867 
MicroF1  0.730  0.634  0.730  0.817  0.840  0.819  0.863  0.867  
Movie (M2M)  MacroF1  0.126  0.111  0.126  0.186  0.189  0.125  0.298  0.310 
MicroF1  0.281  0.276  0.281  0.241  0.245  0.307  0.342  0.352  
Overall  MacroF1  0.430  0.364  0.430  0.501  0.514  0.472  0.581  0.589 
MicroF1  0.506  0.455  0.506  0.540  0.543  0.563  0.603  0.610 
Iva Clustering Results
We first conduct a clustering task to evaluate the performance of the compared methods on DBLP and YAGO Movie datasets. For DBLP, we use the areas of authors as groundtruth label for clustering authors (A2A), and use the areas of venues as labels for clustering venues (V2V). For YAGO Movie, we use the genres of movies as labels (M2M). To be specific, we use
means on the derived metagraphbased relevance matrices for the clustering task. To evaluate the results, we use NMI and purity as evaluation metrics.
Clustering results of the three tasks are shown in Table II. Comparing to previous metagraphbased relevance measures, the proposed GraphSim always shows the best performance of all. We observe at least 19.94% improvement in NMI of GraphSim method when compared with the previous metagraphbased relevance measure on clustering the venues and authors in DBLP, respectively. The clustering results can be sensitive to initialization of centroid seeds, so we set 100 times of random initializations. All methods show worse performance on YAGO Movie than DBLP, but the proposed methods, especially MEGA++, show the best performance comparing to prior works..
IvB Classification Results
We then conduct a classification task. Comparing to the clustering task, in DBLP we do not evaluate the results of classifying the venues, as the total number of venues is only 18. We first apply previous methods and our works to generate the similarity matrices or embedding space of the original network. Then, we randomly partition the samples, and set 80% samples as training set and the rest as testing set. Last, we apply
nearest neighbor (kNN) classifier with to evaluate the methods with training and testing dataset [29, 15]. To prevent the special case of random partition, we repeat and use different random partition 10 times in total. For multilabel classification task, we use the average MacroF1 score and MicroF1 score as the evaluation metrics.GraphSim outperforms the existing relevance measures (e.g StructCount, SCSE and BSCSE) because it represents a better relations between objects in the HINs by normalizing the presence of metagraph structures. MEGA++ outperforms all the baselines because it captures both lowerorder (i.e. metapath) and higherorder (i.e. metagraph) structural information by facilitating the use of coupled tensormatrix decomposition method to obtain a joint embedding for nodes in HINs.
IvC Parameter Analysis
In this section, we first analyze the parameter sensitivity of our methods as shown in Figure 10. We use two evaluation metrics, Normalized Mutual Information(NMI), and Purity (both the larger, the better), to evaluate the performances of our methods for clustering task. In Figure 10 (a)(b), the penalty parameters of MEGA is used for minimizing the Frobenius Norm of embedding space and in Eq. (8), and the best performance is achieved when is set as 3.2768e04. From 10 (b)(c), setting the embedding dimensions as 5 shows the best performance for both MEGA and MEGA++. MEGA++ outperforms MEGA with the same number of embedding dimensions. The Figure 10 (e)(f) show the two penalty parameters and of MEGA++. The penalty parameter of MEGA++ is the same as that in MEGA. The penalty parameter of MEGA++ is used for minimizing the Frobenius Norm of metagraph similarity matrix and its embedding space in Eq. (7). We find that and produce the best performance of clustering task.
1  5  10  15  

DBLP (A2A)  0.338  4.693  9.731  9.689 
DBLP (V2V)  0.129  0.169  0.445  0.667 
MOVIE (M2M)  1.625  6.013  12.69  19.57 
IvD Time Analysis
In this section, we evaluate the execution time of MEGA++. In Table IV, it shows the execution time is linear with respect to the embedding dimensions. Based on the time complexity of MEGA++, when we have a fixed size of dataset, the embedding dimensions , and the number of views are linear with respect to the execution time. Sometimes, MEGA++ can be early stopped when it is already converge, so as to the same time consuming of DBLP (A2A) with and . The same results are shown in the real testing on three tasks, which show the efficiency of MEGA++.
V Related Work
Va Network Embedding
Network embedding want to learn a lowdimensional representations from a network. Previous traditional works [3]
usually construct the affinity graph using the feature vectors of the vertexes and then compute the eigenvectors of the affinity graph. Some other groups use matrix factorization to represent graph as adjacency matrix
[2].Recently, DeepWalk [22] and LINE [31] are proposed for learning the network embedding. Besides these two most popular node embedding methods, many other network embedding are proposed recent years [6, 10, 33, 7]. [6, 33]
learn the node embedding by deep learning encoder methods. However, none previous node embedding methods consider the metagraph and its embedded metapaths information.
VB Tensor Learning and Embedding
Just like deep learning, tensor learning becomes very hot and popular topic in recent years due to the stronger computing capability and lower computation cost [14, 19, 11, 20, 24, 5, 12]. Coupled tensor matrix embedding tries to fuse multiple information sources where matrices and tensors sharing some common modes are jointly embedding [8]. A gradientbased optimization approach for joint tensormatrix analysis is proposed by Acar et al. [1].
VC Multiview Learning
Multiview learning is a hot idea to think one object with different views [26, 27, 23, 13]. In this paper, we think the HIN with different views such as metapaths and metagraph, and fuse the different information for node embedding. However, none of these frameworks can be directly applicable to learn jointly embedding with a partial symmetric tensor and a symmetric matrix, and also do not leverage metapath and metastructure information for similarity search in HINs.
Vi Conclusion and Future Work
In this paper, we proposed a new metagraphbased relevance measure, i.e. GraphSim, and two node embeddings, i.e. MEGA and MEGA++, by leveraging a metagraph and its embedded metapaths similarity information. In the experiment, MEGA++ shows better performance than other compared methods in different tasks. In the future, we can expend our proposed node embedding for a single metagraph to multiple metagraphs node embedding in a HIN. Meanwhile, we can utilize heterogeneous and homogeneous information together for node embedding.
Vii Acknowledge
This work is supported in part by NSFC through grants No. 61503253 and 61672313, NSF through grants No. IIS1526499, IIS1763325, and CNS1626432, and NSF of Guangdong Province through grant No. 2017A030313339.
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