Cross View Link Prediction by Learning Noise-resilient Representation Consensus

Wei, Xiaokai; Xu, Linchuan; Cao, Binggang; Yu, Philip S.

doi:10.1145/3038912.3052575

Cited by 64 publications

(35 citation statements)

References 23 publications

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“…For example, [54] embeds a graph with discrete node attribute value (e.g., the atomic number in a molecule). In contrast, [4] represents the node attribute as a continuous high-dimensional vector (e.g., user attribute features in social networks). [55] deals with both discrete and continuous attributes for nodes and edges.…”

Section: Graph With Auxiliary Informationmentioning

confidence: 99%

“…Sometimes multiple techniques are combined in one study. For example, [4] learns edge-based embedding via minimizing the margin-based ranking loss (Sec Apart from the introduced five categories of techniques, there exist other approaches. [95] presents embedding of a graph by its distances to prototype graphs.…”

Section: Hybrid Techniques and Othersmentioning

confidence: 99%

“…Analysing these graphs provides insights into how to make good use of the information hidden in graphs, and thus has received significant attention in the last few decades. Effective graph analytics can benefit a lot of applications, such as node classification [1], node clustering [2], node retrieval/recommendation [3], link prediction [4], etc. For example, by analysing the graph constructed based on user interactions in a social network (e.g., retweet/comment/follow in Twitter), we can classify users, detect communities, recommend friends, and predict whether an interaction will happen between two users.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Cai

Zheng

Chang

2018

IEEE Trans. Knowl. Data Eng.

1,664

885

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Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.

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Section: Graph With Auxiliary Informationmentioning

confidence: 99%

Section: Hybrid Techniques and Othersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Cai

Zheng

Chang

2018

IEEE Trans. Knowl. Data Eng.

1,664

885

View full text Add to dashboard Cite

show abstract

“…To show the underlying mechanism of the link prediction phase, we first assume that the feature representation G t of the dynamic attributed network until time stamp t is available (which actually not). The original feature representation G t could be very noisy, containing a certain amount of noisy and irrelevant attributes which may degrade the link prediction performance [32,51]. On top of that, the link information in networks may also be noisy and even erroneous from a network analysis perspective [16].…”

Section: Infer Missing Links With Sketching Matrixmentioning

confidence: 99%

Streaming Link Prediction on Dynamic Attributed Networks

Cheng

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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Link prediction targets to predict the future node interactions mainly based on the current network snapshot. It is a key step in understanding the formation and evolution of the underlying networks; and has practical implications in many real-world applications, ranging from friendship recommendation, click through prediction to targeted advertising. Most existing efforts are devoted to plain networks and assume the availability of network structure in memory before link prediction takes place. However, this assumption is untenable as many real-world networks are affiliated with rich node attributes, and often, the network structure and node attributes are both dynamically evolving at an unprecedented rate. Even though recent studies show that node attributes have an added value to network structure for accurate link prediction, it still remains a daunting task to support link prediction in an online fashion on such dynamic attributed networks. As changes in the dynamic attributed networks are often transient and can be endless, link prediction algorithms need to be efficient by making only one pass of the data with limited memory overhead. To tackle these challenges, we study a novel problem of streaming link prediction on dynamic attributed networks and present a novel framework-SLIDE. Methodologically, SLIDE maintains and updates a low-rank sketching matrix to summarize all observed data, and we further leverage the sketching matrix to infer missing links on the fly. The whole procedure is theoretically guaranteed, and empirical experiments on real-world dynamic attributed networks validate the effectiveness and efficiency of the proposed framework.

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“…We can analyze data regarding social network involvement to help improve networks and user experiences. This may explain why there are increasing interests from both industry and academia in personalizing services to users' interests, behaviors and attributes [14] [5], detecting communities [10,33], recommending products [23,43] or friends [12,[34][35][36]. However, the network representation, e.g., adjacency matrix, is usually high-dimensional and spare.…”

Section: Introductionmentioning

confidence: 99%

On Exploring Semantic Meanings of Links for Embedding Social Networks

Wei

Cao

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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There are increasing interests in learning low-dimensional and dense node representations from the network structure which is usually high-dimensional and sparse. However, most existing methods fail to consider semantic meanings of links. Different links may have different semantic meanings because the similarities between two nodes can be different, e.g., two nodes share common neighbors and two nodes share similar interests which are demonstrated in node-generated content. In this paper, the former type of links are referred to as structure-close links while the latter type are referred to as content-close links. These two types of links naturally indicate there are two types of characteristics that nodes expose in a social network. Hence, we propose to learn two representations for each node, and render each representation responsible for encoding the corresponding type of node characteristics, which is achieved by jointly embedding the network structure and inferring the type of each link. In the experiments, the proposed method is demonstrated to be more effective than five recent methods on four social networks through applications including visualization, link prediction and multi-label classification.

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Cross View Link Prediction by Learning Noise-resilient Representation Consensus

Cited by 64 publications

References 23 publications

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Streaming Link Prediction on Dynamic Attributed Networks

On Exploring Semantic Meanings of Links for Embedding Social Networks

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