Community-based link prediction

Biswas, Anupam; Biswas, Bhaskar

doi:10.1007/s11042-016-4270-9

Cited by 37 publications

(7 citation statements)

References 61 publications

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“…Biswas and Biswas considered edge-centrality (EC) measures and communitybased edge-weight (CEW) to define the importance of existing links (Biswas and Biswas 2017). The proposed method improves the intra-community link prediction by assigning positive weight to intra-community links while computing the CEW.…”

Section: Related Workmentioning

confidence: 99%

HM-EIICT: Fairness-aware link prediction in complex networks using community information

2021

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The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions.

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Section: Related Workmentioning

confidence: 99%

HM-EIICT: Fairness-aware link prediction in complex networks using community information

2021

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“…The limitation of this method is it can be applied only on small networks and time-consuming process. In 2017, Anupam and Bhaskar Biswas [27] have presented a prediction based on the community (CLP) for finding missing links, CLP system with centrality between edges, edge centrality with k-path and centrality with the spanning edge. In 2018, Yasami and Safaei [28] presented a novel approach for link prediction and forecasting of upcoming links in dynamic networks using multilayer model.…”

Section: Related Workmentioning

confidence: 99%

A novel similarity measure for missing link prediction in social networks

Chandrika¹,

Reddy²

2020

IJEECS

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Social Networks progress over time by the addition of new nodes and links, form associations with one community to the other community. Over a few decades, the fast expansion of Social Networks has attracted many researchers to pay more attention towards complex networks, the collection of social data, understand the social behaviors of complex networks and predict future conflicts. Thus, Link prediction is imperative to do research with social networks and network theory. The objective of this research is to find the hidden patterns and uncovered missing links over complex networks. Here, we developed a new similarity measure to predict missing links over social networks. The new method is computed on common neighbors with node-to-node distance to get better accuracy of missing link prediction. We tested the proposed measure on a variety of real-world linked datasets which are formed from various linked social networks. The proposed approach performance is compared with contemporary link prediction methods. Our measure makes very effective and intuitive in predicting disappeared links in linked social networks.

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“…However, if the target nodes are not in the same community or have no within-cluster neighbors, their connection likelihood is zero. The Community-based Link Prediction algorithm uses edge centrality and community information in link prediction [50]. In the algorithm, the edge centrality (EC) and communitybased edge weight (CEW) of each existing link are computed, and the overall importance of a common neighbor to a target node is defined as the product of the corresponding EC and CEW.…”

Section: Tablementioning

confidence: 99%

“…However, many of these methods do not deeply investigate the relationship between communities and study the effect of this relationship to link prediction. They just consider that nodes in the same community are more similar and their connection likelihood is greater than that of nodes in different communities [48]- [50]. Ding et al [53] defined the community relevance to measure the level of closeness of two communities.…”

Section: B Community Relationship Strengthmentioning

confidence: 99%

Effective Link Prediction Based on Community Relationship Strength

Fang

Bai

et al. 2019

IEEE Access

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Link prediction is one of the research hotspots in complex network analysis and has a wide range of applications in both theory and reality. To improve the prediction accuracy, this paper proposes a new link prediction framework by considering both node similarity and community information, which overcomes the weaknesses of existing community-based prediction methods. In the proposed framework, a reasonable measure, called community relationship strength (CRS), is defined to estimate the closeness between communities. In this paper, we hold the view that the connection likelihood between two target nodes rests upon not only their similarity but also the closeness of communities that they belong to. Therefore, to measure the connection likelihood, the proposed framework combines CRS with traditional similarity indexes. Three CRS-based methods are derived from the framework. The performance of the CRS-based methods is comprehensively studied on 12 real-world networks compared with several groups of baselines. The experimental results indicate that the CRS-based methods are more effective and robust than others. INDEX TERMS Link prediction, community structure, similarity index, complex networks. I. INTRODUCTION A network is a structure that is composed of nodes and their static or dynamic relations, called edges or links. This simple structure can model a large variety of complex systems such as social, biological and technological systems, in which nodes are entities in these systems and links indicate the relations between entities [1]-[3]. The proliferation of data has inspired the study of network mining and complex network analysis. A wealth of interesting problems related to network mining, such as community detection [4], [5], node rank [6], and network embedding [7], have been studied. Among these interesting network-related problems, link prediction has been receiving increasing interest from disparate disciplines because not only many available real-world networks are incomplete [8], [9], but also its wide range of applications in both theory and reality [10], [11]. For examples, link prediction can be used in exploring network evolution mechanisms [12], [13], finding the cooperation opportunities among scientists [14], [15], recommending friends in social The associate editor coordinating the review of this manuscript and approving it for publication was Lin Wang.

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Community-based link prediction

Cited by 37 publications

References 61 publications

HM-EIICT: Fairness-aware link prediction in complex networks using community information

HM-EIICT: Fairness-aware link prediction in complex networks using community information

A novel similarity measure for missing link prediction in social networks

Effective Link Prediction Based on Community Relationship Strength

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