Link prediction in social networks: the state-of-the-art

Wang, Peng; Xu, Bin; Wu, Yurong; Zhou, Xiaoyu

doi:10.1007/s11432-014-5237-y

Cited by 373 publications

(290 citation statements)

References 118 publications

(158 reference statements)

Supporting

Mentioning

262

Contrasting

Unclassified

Order By: Relevance

“…However, link prediction is very useful to help: infer the underlying complete network (from partially observed structures) (Marchette and Priebe 2008;Kim and Leskovec 2011), understand the evolution of networks (Bringmann et al 2010;Barabâsi et al 2002) and predict hyperlinks in heterogeneous social networks (Zhu et al 2002). Traditionally, most of the approaches for detecting unobserved links are based on topological information, including neighbour-based metrics, path-based metrics and random walk-based metrics (Wang et al 2015). Recent studies have extended such classical metrics by adding weights to the existing links within a topological graph in response to the information obtained from explicitly related sources (Lü and Zhou 2010).…”

Section: Introductionmentioning

confidence: 99%

Inter-variable correlation prediction with fuzzy connected-triples

Shang

Shen

2018

Soft Comput

View full text Add to dashboard Cite

Identification of hidden relationships between domain attributes from different data sources is of great practical significance and forms an emerging field in data mining. However, currently there seldom exist any systematic methods that can effectively handle this problem, especially when dealing with imprecisely described associations. In this paper, a novel data-driven approach for inter-variable correlation prediction is proposed by exploiting the concept of connected-triples. The work is implemented with the use of fuzzy logic. Through the exploitation of link strength measurements and fuzzy inference, the job of detecting similar or related variables can be accomplished via examining link relation patterns within and across different data sources. Empirical evaluation results are discussed, revealing the potential of the proposed work in predicting interesting attribute relations, while involving simple computation mechanisms.

show abstract

Section: Introductionmentioning

confidence: 99%

Inter-variable correlation prediction with fuzzy connected-triples

Shang

Shen

2018

Soft Comput

View full text Add to dashboard Cite

show abstract

“…Mining of network data has become one of the key research areas in recent years [44]. In particular, a lot of attention has been devoted to the link prediction problem in social networks [3,29], where the goal is to discover if two nodes will become connected in the future.…”

Section: Introductionmentioning

confidence: 99%

Newton’s Gravitational Law for Link Prediction in Social Networks

Ashraf

Budka

Musial-Gabrys

2017

Studies in Computational Intelligence

View full text Add to dashboard Cite

Link prediction is an important research area in network science due to a wide range of real-world application. There are a number of link prediction methods. In the area of social networks, these methods are mostly inspired by social theory, such as having more mutual friends between two people in a social network platform entails higher probability of those two people becoming friends in the future. In this paper we take our inspiration from a different area, which is Newton's law of universal gravitation. Although this law deals with physical bodies, based on our intuition and empirical results we found that this could also work in networks, and especially in social networks. In order to apply this law, we had to endow nodes with the notion of mass and distance. While node importance could be considered as mass, the shortest path, path count, or inverse similarity (AdamicAdar, Katz score etc.) could be considered as distance. In our analysis, we have primarily used degree centrality to denote the mass of the nodes, while the lengths of shortest paths between them have been used as distances. In this study we compare the proposed link prediction approach to 7 other methods on 4 datasets from various domains. To this end, we use the ROC curves and the AUC measure to compare the methods. As the results show that our approach outperforms the other 7 methods on 2 out of the 4 datasets, we also discuss the potential reasons of the observed behaviour.

show abstract

“…In addition, dynamic real-world graphs include social networks. The dynamicity of graphs stems from either the world-wide hot events or updates of the web contents [2]. Thus, the rapid explosion of data volume and dynamicity urgently necessitates large scale graph analysis applications which can handle these dynamic workloads.…”

Section: Introductionmentioning

confidence: 99%

An Association-Oriented Partitioning Approach for Streaming Graph Query

Hao

Yuan

et al. 2017

Scientific Programming

View full text Add to dashboard Cite

The volumes of real-world graphs like knowledge graph are increasing rapidly, which makes streaming graph processing a hot research area. Processing graphs in streaming setting poses significant challenges from different perspectives, among which graph partitioning method plays a key role. Regarding graph query, a well-designed partitioning method is essential for achieving better performance. Existing offline graph partitioning methods often require full knowledge of the graph, which is not possible during streaming graph processing. In order to handle this problem, we propose an association-oriented streaming graph partitioning method named Assc. This approach first computes the rank values of vertices with a hybrid approximate PageRank algorithm. After splitting these vertices with an adapted variant affinity propagation algorithm, the process order on vertices in the sliding window can be determined. Finally, according to the level of these vertices and their association, the partition where the vertices should be distributed is decided. We compare its performance with a set of streaming graph partition methods and METIS, a widely adopted offline approach. The results show that our solution can partition graphs with hundreds of millions of vertices in streaming setting on a large collection of graph datasets and our approach outperforms other graph partitioning methods.

show abstract

Link prediction in social networks: the state-of-the-art

Abstract: Advanced strain engineering for state-of-the-art nanoscale CMOS technology SCIENCE CHINA Information Sciences 54, 946 (2011); The state-of-the-art of the China Seismo-Electromagnetic Satellite mission

Cited by 373 publications

References 118 publications

Inter-variable correlation prediction with fuzzy connected-triples

Inter-variable correlation prediction with fuzzy connected-triples

Newton’s Gravitational Law for Link Prediction in Social Networks

An Association-Oriented Partitioning Approach for Streaming Graph Query

Contact Info

Product

Resources

About