Link Prediction in Complex Networks: A Mutual Information Perspective

Tan, Fei; Xia, Yongxiang; Zhu, Boyao

doi:10.1371/journal.pone.0107056

Cited by 128 publications

(77 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Link prediction evaluates the possibility of existence of future links between vertices by observing vertices and links attributes in the network. Link prediction is used to detect missing and fake links and predicts future existence of the links with the development of network [14].…”

Section: Applications Of Community Detection:-mentioning

confidence: 99%

COMMUNITY DETECTION on SOCIAL MEDIA using GRAPH BASED APPROACH.

Raman¹,

Kanal.²

2016

IJAR

View full text Add to dashboard Cite

Social media has followed an exponential graph over the past few years with incorporating features which at one time seemed impossible. The social media has had an enduring effect on the thought process of the general populace. With the diverse nature of the population which take part in the daily chatting, tagging, posting and uploading on the virtual world, the study of such coalesce of communities. This paper aims at the mining and analysis of the communities with focus on the techniques used for the detection process. We discuss four methods of detection, beginning with the node-centric moving on to group centric, then to network centric and concluding with hierarchy centric method of detection. This paper also briefly discusses the applications of community detection in varied fields.Copy Right, IJAR, 2016,. All rights reserved. …………………………………………………………………………………………………….... Introduction:-The past decade has witnessedthe rapid development of social networking sites which has empowered new ways of collaboration and communication. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale [2]. Hence, study of social network is of great importance in sociology, biology and computer science. Social network analysis is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. Social network analysis provides both a visual and a mathematical analysis of human relationships. A valuable tool in the analysis of large complex networks is community detection. Community Detection:-Community is formed by individuals such that those within a group interact with each other more frequently than with those outside the group [1]. There are two types of communities in social networksExplicit groups which are formed as a result of conscious human decision. Implicit groups which emerge from interactions and activities of users. Often communities are defined with respect to a graph, which consists of set of objects called vertices (V) and their relations called as edges (E). Therefore, according to computer science, community detection is identifying a group of vertices that are more densely connected to each other than the rest of the network [1]. Figure below shows a network with three communities.

show abstract

Section: Applications Of Community Detection:-mentioning

confidence: 99%

COMMUNITY DETECTION on SOCIAL MEDIA using GRAPH BASED APPROACH.

Raman¹,

Kanal.²

2016

IJAR

View full text Add to dashboard Cite

show abstract

“…The first category is based on various generating mechanisms, for instance the common neighbours (CN) algorithm 17–19 and the cannistraci-resouce-allocation algorithm (CRA, which can be also called Cannistraci-Hebb network automata model (CH) based on local community paradigm) 20–22 . Their computational complexity is relatively low, while precision is not so satisfying.…”

Section: Introductionmentioning

confidence: 99%

Link Prediction based on Quantum-Inspired Ant Colony Optimization

Cao

Zhang

Guan

et al. 2018

Sci Rep

View full text Add to dashboard Cite

Incomplete or partial observations of network structures pose a serious challenge to theoretical and engineering studies of real networks. To remedy the missing links in real datasets, topology-based link prediction is introduced into the studies of various networks. Due to the complexity of network structures, the accuracy and robustness of most link prediction algorithms are not satisfying enough. In this paper, we propose a quantum-inspired ant colony optimization algorithm that integrates ant colony optimization and quantum computing to predict links in networks. Extensive experiments on both synthetic and real networks show that the accuracy and robustness of the new algorithm is competitive in respect to most of the state of the art algorithms. This result suggests that the application of intelligent optimization to link prediction is promising for boosting its accuracy and robustness.

show abstract

“…J. Kim, et al [10] make use of cluster information. And F. Tan, et al [11] reexamine the role of topology feature from the perspective of information theory. These work just consider features from one or two sources.…”

mentioning

confidence: 99%

Link Prediction in Microblog Network Using Supervised Learning with Multiple Features

Han¹,

Xu²

2016

JCP

View full text Add to dashboard Cite

Link prediction (LP) is a fundamental network analysis task. It aims to analyze the existing links and predict the missing or potential relations between users in a social network. It can help users in finding new friends, enhance their loyalties to the web sites and build a healthy social environment. In previous researches, much attention was focused on structure information or node attributes, in order to analyze the global or local properties. Considering the nature of Microblog social network, we proposed a link prediction system combining multiple features from different perspectives, and learn a classifier from these feature subsets to predict the potential links. We train classifiers using SVM, Naï ve Bayes, and Random Forest and Logistic Regression algorithms and evaluate them using the microblog network dataset. The results show that our features perform better than the traditional features, and the combination of multiple features can achieve highest accuracy.

show abstract

Link Prediction in Complex Networks: A Mutual Information Perspective

Cited by 128 publications

References 49 publications

COMMUNITY DETECTION on SOCIAL MEDIA using GRAPH BASED APPROACH.

COMMUNITY DETECTION on SOCIAL MEDIA using GRAPH BASED APPROACH.

Link Prediction based on Quantum-Inspired Ant Colony Optimization

Link Prediction in Microblog Network Using Supervised Learning with Multiple Features

Contact Info

Product

Resources

About