Document Network Embedding: Coping for Missing Content and Missing Links

by   Jean Dupuy, et al.

Searching through networks of documents is an important task. A promising path to improve the performance of information retrieval systems in this context is to leverage dense node and content representations learned with embedding techniques. However, these techniques cannot learn representations for documents that are either isolated or whose content is missing. To tackle this issue, assuming that the topology of the network and the content of the documents correlate, we propose to estimate the missing node representations from the available content representations, and conversely. Inspired by recent advances in machine translation, we detail in this paper how to learn a linear transformation from a set of aligned content and node representations. The projection matrix is efficiently calculated in terms of the singular value decomposition. The usefulness of the proposed method is highlighted by the improved ability to predict the neighborhood of nodes whose links are unobserved based on the projected content representations, and to retrieve similar documents when content is missing, based on the projected node representations.



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1. Introduction

Searching through a network of documents, such as scientific articles (Brochier, 2019), Web pages or social media posts (Guille et al., 2013)

, is a common task and a long-standing research topic. Recent works propose to improve the performance of information retrieval systems by leveraging dense vector space representations of the content and the nodes

(Wen et al., 2018; Seyler et al., 2018, 2018; Zhang et al., 2019; Makarov et al., 2018). Quite often, for various reasons, such as technical issues or legal limitations (e.g. paywall), not all links can be observed nor can all the content be retrieved.

These representations are learned with algorithms similar to those devised for word embedding, e.g. DeepWalk (Perozzi et al., 2014), Node2Vec (Grover and Leskovec, 2016) or GVNR (Brochier et al., 2019) for node representations, or Doc2Vec (Le and Mikolov, 2014) for content. Obviously, missing content prevents applying any document embedding techniques. Also, since network embedding algorithms perform random walks to measure the co-occurrence frequency between nodes, they cannot learn representations of isolated nodes.

In this paper, we address this issue by formulating a translation task, from available content representations to node representations, and from available node representations to content representations. Assuming that the topology of a network of documents correlates with the content of the documents, we propose to estimate the missing node (resp. document) representations as a linear transformation, i.e. projection, of the content (resp. node) representations. More specifically, we show how to construct a dictionary of pairs of content and node representations that allows learning a projection matrix, inspired by recent advances in machine translation with pre-trained word embeddings (Mikolov et al., 2013b; Xing et al., 2015; L. Smith et al., 2017). Note that a self-translation network embedding algorithm, STNE (Liu et al., 2018), has been recently proposed, however it addresses a different task. It learns to translate a sequence of content to a sequence of nodes to learn richer node representations and it cannot operate on isolated nodes nor deal with missing content.

Our approach proves relevant, as evidenced by the quality of the alignment between the estimated representations and the true representations. Practically speaking, our method has two main applications. On the one hand, estimating the node representation helps in recovering the missing links. On the other hand, estimating the content representation from the node representation allows measuring the similarity with the content of other documents and thus provides helpful clues about the missing content.

The rest of this paper is organized as follows. In section 2, we survey related work on document and network embedding, as well as embedding-based bilingual translation. We then describe our proposal to estimate the missing representations in section 3. Next, in section 4, we evaluate the quality of the projection and assess the improvement brought by our method in predicting the neighborhood of nodes whose links are missing and retrieve documents similar to those without content. Finally, we conclude and provide future directions in section 5.

2. Related Work

Word embedding, i.e., the task of learning dense vector space representations of words, is tightly connected to the tasks of learning document and node representations, i.e. document embedding and network embedding. In this section, we first briefly survey works related to word embedding and then present models adapted for document and network embedding. Lastly, we cover recent advances in offline bilingual translation via word embeddings, on which our work is based.

2.1. Word Embedding

Word embedding builds upon the distributional hypothesis. It states that distributional similarity and meaning similarity are correlated, which allows learning representations of words based on the contexts in which they occur, the context of a word being co-occurring words in a short window.

Mikolov et al. (Mikolov et al., 2013a)

propose two models, namely Skip-Gram and Continuous Bag-of-words (CBOW). The first models the conditional probability of occurrence of a word, given a word in the context. The second models the conditional probability of occurrence of a word, given the set of words in the context. In both cases, the probability is defined as a softmax function parameterized by the dot products of the vector representations of the words.

Because the softmax makes the estimation of the word representations computationally expensive, Mikolov et al. (Mikolov et al., 2013c)

propose approximate solutions based either on the hierarchical softmax or negative sampling, an approximation akin to noise-contrastive estimation.

2.2. Content Representation Learning

Le and Mikolov (Le and Mikolov, 2014) extends the Skip-Gram and CBOW models to learn representations of documents, under the names, respectively, Distributed Bag-of-words (DV-DBOW) and Distributed Memory (DV-DM). They rely on a simple trick, that consists in considering each document as a distinct new vocabulary entry, that occurs in every window in the document. Thus, these models learn document representations that are good at predicting the words they contain.

2.3. Node Representation Learning

Even though the distributional hypothesis originated in linguistics, Perozzi et al. (Perozzi et al., 2014) show that sequences of nodes generated by random walks and sentences share some statistical properties. In particular, they show that the frequency at which nodes appear in short random walks follows a power-law distribution, like the frequency of words in language. Consequently, they propose to learn node representations under the distributional hypothesis. The proposed model, DeepWalk, consists in learning the representations based on the Skip-Gram model with the hierarchical softmax approximation, from a corpus of node sequences – deemed equivalent to sentences – generated by truncated random walks.

LINE (Tang et al., 2015)

refines the objective optimized by DeepWalk, trying to preserve both the first and second order proximities in the embedding space. The representations are learned based on the Skip-Gram model with negative sampling rather than the hierarchical softmax approximation, using a specific stochastic gradient descent algorithm for weighted networks, that samples the edges according to their weights.

Node2vec (Grover and Leskovec, 2016) performs biased random walks, in order to better balance the exploration-exploitation trade-off, arguing that the added flexibility in exploring neighborhoods helps learning richer representations. The representations are also learned based on the Skip-Gram model with negative sampling.

2.4. Bilingual Translation

Mikolov et al. (Mikolov et al., 2013b) first propose to learn a linear transformation from the embedding space of a source language into the embedding space of a target language. They suggest to estimate a projection matrix from pre-trained vectors and a bilingual dictionary. To this end they formulate a least square problem based on the Euclidean distance between aligned vectors, which they solve with the regular stochastic gradient descent algorithm.

Even though this approach yields encouraging results, Xing et al. (Xing et al., 2015)

argue that the problem is ill-posed, since the similarity between representations is usually measured in terms of the cosine similarity, rather than the Euclidean distance. They show how to learn unitary word embeddings and formulate a maximization problem in terms of the dot product between some source vectors and projected target vectors. They also require the projection matrix to be orthogonal, so that the projected vectors remain unitary in order to preserve the equivalence between the dot product and the cosine similarity. Eventually, they devise a gradient descent based algorithm to learn the projection matrix. Yet, their approach is computationally expensive, because it requires to orthogonalize the projection matrix after each update, by computing its singular value decomposition.

Smith et al. (L. Smith et al., 2017) show that finding the projection matrix that solves the problem stated by Xing et al. is actually equivalent to solving a least square problem based on the Euclidean distance, when the vectors are unitary and the projection matrix is orthogonal. Furthermore, they link this problem with the orthogonal Procrustes problem, which admits an analytical solution. The projection matrix is obtained from the singular value decomposition of the similarity matrix between the aligned source and target vectors.

3. Proposed Method

In this section, we describe our proposal, which consists in adapting techniques devised for bilingual translation to learn a projection from the content representations (pre-trained with, e.g. Doc2Vec) to the node representations (pre-trained with, e.g. DeepWalk), and vice versa.

Consider a corpus of documents organized in a network, so that the content of some documents is missing and the links of some other documents are unobserved. We assume a set of pre-trained node representations and a set of document representations . Our goal is to estimate the missing node representations in , from the content representations in ; conversely, estimate the missing content representations in , from the node representations in .

We posit that the topology of the network and the content of the documents correlate. Thus, we aim at finding a projection matrix that maps content representations to node representations, and node representations to content representations. More particularly, given a document , we want to maximize the cosine of the angle between and . That is to say, we want to find a linear transformation so that the vectors and point in the same direction. More formally, based on a set of pairs of node representations and document representations, , we want to find a matrix so that:


In the rest of this section, we describe how to construct and then detail how to learn the projection matrix .

3.1. Dictionary construction

Rather than learning the projection from all the available pairs of node representations and content representations, we suggest to restrain to a specific subset. The aim in doing so is (i) favor the learning of a good projection and (ii) limit the computational cost.

Bojanowski et al. (Bojanowski et al., 2017) note that Skip-Gram, the model behind most network embedding algorithms, struggles to learn good representations for rare words. This also holds for network embedding algorithms, since Perrozi et al. (Perozzi et al., 2014) show that the frequency at which nodes occur in short random walks and the frequency of words in language follow a similar law. Hence, we propose to avoid rare nodes and to construct the dictionary only from the nodes that occur the most in short random walks on the network. Formally, with the frequency of node , we construct the set of cardinality that maximizes .

3.2. Projection Learning

Following (Xing et al., 2015), we rewrite formula 1 in terms of the dot product, and thus require the node and content representation to be unitary vectors and constrain to be orthogonal:


Yet, rather than learning the representations again as Xing et al. suggest, we simply row-normalize and . Then, thanks to the proportional relationship between the opposite of the dot product and the squared Euclidean distance highlighted in (L. Smith et al., 2017), because , we transform the problem into a minimization one:


This is a classic linear algebra problem, which solution is given in (Schönemann, 1966). It consists in calculating the singular value decomposition of the matrix , with and the aligned node and content representations according to the dictionary . With , the matrix is defined as:


We estimate the node representation of an isolated node by calculating . Similarly, we estimate the content representation of a document whose content is missing by calculating .

4. Experiments

In this section, we evaluate our proposal on two networks of documents. First, we focus on missing links and show that estimating the node representations from the content representations allows to better reconstruct the neighborhood of nodes whose links are unobserved. Second, we focus on missing content and show that estimating the content representations from node representations allows to retrieve a significant fraction of the similar documents.

4.1. Experimental setup

4.1.1. Data

We consider two well known networks of scientific articles, Cora (McCallum et al., 2000) and HepTh (Leskovec et al., 2005), whose properties are summed up in Table 1. No content nor citation links are missing. These datasets available online111Get the data:,

Name # of documents # of links
Cora 2,211 5,001
HepTh 1,038 1,974
Table 1. Network properties.

4.1.2. Node and Content Representations

We experiment with DeepWalk (Perozzi et al., 2014) to learn the node representations, with the following configuration: 80 random walks length 80 from each node, and a co-occurrence window of size 10. Regarding content representation, we experiment with the Distributed Memory (DV-DM) variant of Doc2Vec (Le and Mikolov, 2014) with a window of size 10. Here after, we report the results for representations of size 500.

4.1.3. Projection Learning

We learn the projection matrix via the exact singular value decomposition, based on a dictionary of size 1400 for Cora, and a dictionary of size 550 for HepTh.

4.1.4. Similarity Metric

We measure the similarity between vector representations in terms of cosine similarity (Mikolov et al., 2013a).

4.2. Missing Links: Neighborhood Reconstruction

We begin by assessing the quality of the estimated node representations. The evaluation task consists in reconstructing the neighborhood of a document whose links are unobserved. We adopt a leave-one-out strategy and proceed in the following manner. First, we learn all the content representations from the entire corpus. Then, for each document , we do the following:

  1. Hide all the incoming and outgoing links of document ;

  2. Learn the node representations on this sub-network;

  3. Learn the projection matrix on this sub-network;

  4. Measure the similarity between the estimated node representation and all the other node representations ;

  5. Order the documents accordingly and measure the precision at , w.r.t. the true links.

We report the relative gain in average precision in Table 2, w.r.t. two baselines:

  • : Documents ordered according to the similarity between the content representations;

  • Random: Documents ordered randomly, picked among the 200 documents with the highest degrees.

It reveals that the estimated node representations, obtained via the linear transformation of the content representations always lead to a better precision in predicting the neighborhood of a document.

P@5 P@10 P@20 P@50
Cora +4.4% +23.4% +20.1% +20.0%
Random +681.4% +475.9% +166.1% +51.9%
Hepth +69.2% +42.9% +5.9% +2.6%
Random +516.6% +375.1% +350.4% +175.6%
Table 2. Precision gain in link prediction.

4.3. Missing Content: Similar Document Retrieval

We now switch our attention to missing content. We assess the quality of the estimated content representations following a similar leave-one-out methodology. We learn all the the content representations and define the true sets of similar document . The evaluation task consists in reconstructing the set of similar documents for a document whose content is unknown. We proceed as follows. First, we learn all the node representations from the whole network. Then, for each document , we do the following:

  1. Hide document from the corpus;

  2. Retrain the content representations on this sub-corpus;

  3. Learn the projection matrix on this sub-corpus;

  4. Measure the similarity between the estimated content representation and all the content representations ;

  5. Measure the accuracy by comparing the true set of similar documents and the estimated set of similar documents .

We report the average precision following this procedure in the first row of Table 3. The second row shows the average precision based on the node representations, where . It reveals that estimating the missing content representation from the corresponding node representation doubles the accuracy in document retrieval, in comparison with the accuracy obtained by measuring the similarity based on the node representations. In particular, we are able to recover, on average, two-thirds of the similar documents in the Hepth corpus.

Cora Hepth
0.36 0.65
0.13 0.30
Table 3. Average accuracy in similar document retrieval.

5. Conclusion

In this paper, we described a way to efficiently learn a linear function to map pre-trained node and content representations learned from a network of documents. In presence of missing links and missing content, this method helps in reconstructing the neighborhood of isolated documents and also helps in recovering documents that are similar to a document whose content is unknown. In future work, we would like to investigate how to learn a non-linear transformation to refine the mapping between the two spaces.


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