Accurate similarity index based on activity and connectivity of node for link prediction

Li, Longjie; Qian, Lvjian; Wang, Xiaoping; Luo, Shishun; Chen, Xiaoyun

doi:10.1142/s0217979215501088

Cited by 11 publications

(3 citation statements)

References 37 publications

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“…Complex networks are pervasive in many fields, including biology [11], physics, computer science and others. Presently, the research encompasses important node identification [12], community discovery [13], link prediction [14], etc. Important node identification is a key problem in complex networks [15].…”

Section: Discussionmentioning

confidence: 99%

Research on Recognition Algorithm of important Nodes in Complex Network

Su¹

2020

IJCAI

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It is crucial to identify the important nodes in the network. On the basis of the K-shell algorithm, this study researched the recognition of important nodes in complex networks. The study introduces concepts of edge weight and influence coefficient, the design of an IKS algorithm, and an analysis of its recognition effect in Zachary network and real micro blog network. The results showed that the partition results of the K-shell algorithm were coarse, while the partition results of the IKS algorithm were refined; the IKS algorithm could sort the important nodes accurately on the basis of the K-shell algorithm, and its rationality was higher than that of the closeness centralization and PaperRank algorithm. The partition results in the microblog network also verified the effectiveness of the improved method. The experimental results show that the IKS algorithm is reliable in the important node identification, which is beneficial to the recognition of important nodes in complex network. Povzetek: V študentskem prispevku je opisan študij algoritma za določitev pomembnih vozlišč v kompleksnih omrežjih.

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Section: Discussionmentioning

confidence: 99%

Research on Recognition Algorithm of important Nodes in Complex Network

Su¹

2020

IJCAI

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“…Besides learning-based methods, an ocean of similaritybased methods have been proposed due to their simplicity and feasibility [10], [11], [30]. The simplest one is the Preferential Attachment index [31], which defines the similarity score between two target nodes as the product of their degrees.…”

Section: Tablementioning

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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“…As a fundamental research hotspot in complex network analysis, link prediction has a wide range of applications in both theory and reality, such as analysis of network evolution [1,2], recommendation system [3], and checking potential interactions between proteins in biological networks [4,5]. The basic task of link prediction is to estimate the missing or latent existent links between unconnected nodes in a network [6,7]. To date, a host of algorithms and models have been proposed for link prediction [6,8,9].…”

Section: Introductionmentioning

confidence: 99%

Predicting Missing Links Based on a New Triangle Structure

Bai

Cheng

et al. 2018

Complexity

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With the rapid growth of various complex networks, link prediction has become increasingly important because it can discover the missing information and predict future interactions between nodes in a network. Recently, the CAR and CCLP indexes have been presented for link prediction by means of different triangle structure information. However, both indexes may lose the contributions of some shared neighbors. We propose in this work a new index to make up the weakness and then improve the accuracy of link prediction. The proposed index focuses on a new triangle structure, i.e., the triangle formed by one seed node, one common neighbor, and one other node. It emphasizes the importance of these triangles but does not ignore the contribution of any common neighbor. In addition, the proposed index adopts the theory of resource allocation by penalizing large-degree neighbors. The results of comparison with CN, AA, RA, ADP, CAR, CAA, CRA, and CCLP on 12 real-world networks show that the proposed index outperforms the compared methods in terms of AUC and ranking score.

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Accurate similarity index based on activity and connectivity of node for link prediction

Cited by 11 publications

References 37 publications

Research on Recognition Algorithm of important Nodes in Complex Network

Research on Recognition Algorithm of important Nodes in Complex Network

Effective Link Prediction Based on Community Relationship Strength

Predicting Missing Links Based on a New Triangle Structure

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