Link prediction in dynamic social networks by integrating different types of information

With the rapid development of the Internet, the computational analysis of social networks has grown to be a salient issue. Various research analyses social network topics, and a considerable amount of attention has been devoted to the issue of link prediction. Link prediction aims to predict the interactions that might occur between two entities in the network. To this aim, this study proposed a novel path and node combined approach and constructed a methodology for measuring node similarities. The method was illustrated with five real datasets obtained from different types of social networks. An extensive comparison of the proposed method against existing link prediction algorithms was performed to demonstrate that the path and node combined approach achieved much higher mean average precision (MAP) and area under the curve (AUC) values than those that only consider common nodes (e.g. Common Neighbours and Adamic/Adar) or paths (e.g. Random Walk with Restart and FriendLink). The results imply that two nodes are more likely to establish a link if they have more common neighbours of lower degrees. The weight of the path connecting two nodes is inversely proportional to the product of degrees of nodes on the pathway. The combination of node and topological features can substantially improve the performance of similarity-based link prediction, compared with node-dependent and path-dependent approaches. The experiments also demonstrate that the path-dependent approaches outperform the node-dependent appraoches. This indicates that topological features of networks may contribute more to improving performance than node features.

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Link Prediction in Dynamic Social Networks: A Literature Review

2018 IEEE 5th International Congress on Information Science and Technology (CiSt) ◽

10.1109/cist.2018.8596511 ◽

2018 ◽

Cited By ~ 2

Author(s):

Mohammad Marjan ◽

Nazar Zaki ◽

Elfadil A. Mohamed

Keyword(s):

Social Networks ◽

Literature Review ◽

Link Prediction ◽

Dynamic Social Networks

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A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

Symmetry ◽

10.3390/sym12010100 ◽

2020 ◽

Vol 12 (1) ◽

pp. 100

Author(s):

Xinyu Huang ◽

Dongming Chen ◽

Tao Ren

Keyword(s):

Social Networks ◽

Gravity Model ◽

Link Prediction ◽

Temporal Networks ◽

Scientific Society ◽

Multidisciplinary Study ◽

Dynamic Social Networks ◽

Traditional Research ◽

Real World Datasets ◽

Social Applications

Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications.

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Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

Advances in Science Technology and Engineering Systems Journal ◽

10.25046/aj040631 ◽

2019 ◽

Vol 4 (6) ◽

pp. 244-254

Author(s):

Elfadil Abdalla Mohameds ◽

Nazar Zakis ◽

Mohammad Marjans

Keyword(s):

Social Networks ◽

Literature Review ◽

Link Prediction ◽

Prediction Methods ◽

Current Trends ◽

Dynamic Social Networks

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Social Networks and the East Indiaman

Between Monopoly and Free Trade ◽

10.23943/princeton/9780691159065.003.0004 ◽

2014 ◽

Author(s):

Emily Erikson

Keyword(s):

Social Networks ◽

Organizational Context ◽

Individual Behavior ◽

Personal History ◽

Relative Importance ◽

Operational Decisions ◽

Different Types ◽

History Of ◽

Types Of Information ◽

Source Of Information

This chapter identifies how organizational context affects individual behavior and the propensity to incorporate information from peers into important operational decisions. The identification of this relationship illuminates, in turn, the larger question of how—or the mechanisms by which—decentralization contributed to the pattern of innovation found in the English Company. The analysis focuses on evaluating the relative importance of different types of information in leading captains to choose trade in one port over another, including formal orders given by the Company, a personal history of contact with ports, and information transmitted via social networks from ship to ship. The results show that social networks were an important source of information for captains when deciding which port to travel to next.

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