A<scp>sp</scp>E<scp>m</scp>: Embedding Learning by Aspects in Heterogeneous Information Networks

Shi, Yu; Gui, Huan; Zhu, Qi; Kaplan, Lance; Han, Jiawei

doi:10.1137/1.9781611975321.16

Cited by 88 publications

(68 citation statements)

References 31 publications

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“…Most of the efforts are designed for homogeneous networks and are inadequate to handle heterogeneous networks. Het-erogeneous network representation learning methods include metapath2vec [6], metagraph2vec [37], PTE [28], HNE [4], LANE [11], and ASPEM [23]. Except metapath2vec and metagraph2vec, none of these methods is aligned to our problem of generic unsupervised task-independent network embedding learning preserving the heterogeneity in structure and semantics, as discussed in Section I.…”

Section: Related Workmentioning

confidence: 99%

“…A more recent work proposed metagraph2vec [37] that leverages metagraph in order to capture richer structural contexts and semantics between distant nodes. Other heterogeneous network embedding methods include PTE [28] that is a semi-supervised representation learning method for text data; HNE [4] that learns representation for each modality of the network separately and then unifies them into a common space using linear transformations; LANE [11] that generates embeddings for attributed networks; and AS-PEM [23] that captures the incompatibility in heterogeneous networks by decomposing the input graph into multiple aspects and learns embeddings independently for each aspect. None of PTE, HNE, LANE, or ASPEM is aligned to the generic task of task-independent heterogeneous network embedding learning.…”

Section: Introductionmentioning

confidence: 99%

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motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks

Dareddy

Das

Yang

2019

2019 IEEE International Conference on Big Data (Big Data)

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Recent years have witnessed a surge of interest in machine learning on graphs and networks with applications ranging from vehicular network design to IoT traffic management to social network recommendations. Supervised machine learning tasks in networks such as node classification and link prediction require us to perform feature engineering that is known and agreed to be the key to success in applied machine learning. Research efforts dedicated to representation learning, especially representation learning using deep learning, has shown us ways to automatically learn relevant features from vast amounts of potentially noisy, raw data. However, most of the methods are not adequate to handle heterogeneous information networks which pretty much represents most real world data today. The methods cannot preserve the structure and semantic of multiple types of nodes and links well enough, capture higherorder heterogeneous connectivity patterns, and ensure coverage of nodes for which representations are generated. In this paper, we propose a novel efficient algorithm, motif2vec that learns node representations or embeddings for heterogeneous networks. Specifically, we leverage higher-order, recurring, and statistically significant network connectivity patterns in the form of motifs to transform the original graph to motif graph(s), conduct biased random walk to efficiently explore higher order neighborhoods, and then employ heterogeneous skip-gram model to generate the embeddings. Unlike previous efforts that uses different graph meta-structures to guide the random walk, we use graph motifs to transform the original network and preserve the heterogeneity. We evaluate the proposed algorithm on multiple real-world networks from diverse domains and against existing state-of-theart methods on multi-class node classification and link prediction tasks, and demonstrate its consistent superiority over prior work.Author Terms − heterogeneous information networks, network embedding, network representation learning, feature learning, motifs

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks

Dareddy

Das

Yang

2019

2019 IEEE International Conference on Big Data (Big Data)

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“…For DBLP, we use the manual class labels of authors from four research areas, i.e., database, data mining, machine learning and information retrieval provided by [1]. For IMDB, we follow [17] to use all 23 available genres such as drama, comedy, romance, thriller, crime and action as class labels. For Yelp, we extract six sets of businesses based on some available attributes, i.e., good for kids, take out, outdoor seating, good for groups, delivery and reservation.…”

Section: A Experimental Settingsmentioning

confidence: 99%

Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights

Yang

Feng

Pan

et al. 2018

2018 IEEE International Conference on Data Mining (ICDM)

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Heterogeneous information network (HIN) has drawn significant research attention recently, due to its power of modeling multi-typed multi-relational data and facilitating various downstream applications. In this decade, many algorithms have been developed for HIN modeling, including traditional similarity measures and recent embedding techniques. Most algorithms on HIN leverage meta-graphs or meta-paths (special cases of meta-graphs) to capture various semantics. Given any arbitrary set of meta-graphs, existing algorithms either consider them as equally important or study their different importance through supervised learning. Their performance largely relies on prior knowledge and labeled data. While unsupervised embedding has shown to be a fundamental solution for various homogeneous network mining tasks, for HIN, it is a much harder problem due to such a presence of various meta-graphs.In this work, we propose to study the utility of different meta-graphs, as well as how to simultaneously leverage multiple meta-graphs for HIN embedding in an unsupervised manner. Motivated by prolific research on homogeneous networks, especially spectral graph theory, we firstly conduct a systematic empirical study on the spectrum and embedding quality of different meta-graphs on multiple HINs, which leads to an efficient method of meta-graph assessment. It also helps us to gain valuable insight into the higher-order organization of HINs and indicates a practical way of selecting useful embedding dimensions. Further, we explore the challenges of combining multiple meta-graphs to capture the multi-dimensional semantics in HIN through reasoning from mathematical geometry and arrive at an embedding compression method of autoencoder with 2,1-loss, which finds the most informative meta-graphs and embeddings in an end-to-end unsupervised manner. Finally, empirical analysis suggests a unified workflow to close the gap between our meta-graph assessment and combination methods. To the best of our knowledge, this is the first research effort to provide rich theoretical and empirical analyses on the utility of meta-graphs and their combinations, especially regarding HIN embedding. Extensive experimental comparisons with various state-of-the-art neural network based embedding methods on multiple real-world HINs demonstrate the effectiveness and efficiency of our framework in finding useful meta-graphs and generating high-quality HIN embeddings.

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“…As a large number of social and information networks are heterogeneous in nature, i.e., nodes and edges are of multiple types, heterogeneous network embedding methods have recently garnered attention [29][30][31]35]. They learn node embeddings by exploiting various types of relationships among nodes and the network structure, and use them for general downstream tasks, such as node classification [7,26], link prediction [9,27], and clustering [3,15].…”

Section: Introductionmentioning

confidence: 99%

Task-Guided Pair Embedding in Heterogeneous Network

Park

Kim

Zhu

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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Many real-world tasks solved by heterogeneous network embedding methods can be cast as modeling the likelihood of a pairwise relationship between two nodes. For example, the goal of author identification task is to model the likelihood of a paper being written by an author (paper-author pairwise relationship). Existing taskguided embedding methods are node-centric in that they simply measure the similarity between the node embeddings to compute the likelihood of a pairwise relationship between two nodes. However, we claim that for task-guided embeddings, it is crucial to focus on directly modeling the pairwise relationship. In this paper, we propose a novel task-guided pair embedding framework in heterogeneous network, called TaPEm, that directly models the relationship between a pair of nodes that are related to a specific task (e.g., paper-author relationship in author identification). To this end, we 1) propose to learn a pair embedding under the guidance of its associated context path, i.e., a sequence of nodes between the pair, and 2) devise the pair validity classifier to distinguish whether the pair is valid with respect to the specific task at hand. By introducing pair embeddings that capture the semantics behind the pairwise relationships, we are able to learn the fine-grained pairwise relationship between two nodes, which is paramount for task-guided embedding methods. Extensive experiments on author identification task demonstrate that TaPEm outperforms the state-of-the-art methods, especially for authors with few publication records.

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AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks

Cited by 88 publications

References 31 publications

motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks

motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks

Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights

Task-Guided Pair Embedding in Heterogeneous Network

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