Triadic Closure Pattern Analysis and Prediction in Social Networks

Huang, Hong; Tang, Jie; Liu, Lu; Luo, Jar‐Der; Fu, Xiaoming

doi:10.1109/tkde.2015.2453956

Cited by 61 publications

(22 citation statements)

References 37 publications

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“…The triadic structure is the most basic group structure in social networks. A triad is a group of three people [33]. Roughly speaking, there are two types of triads: closed triads and open triads (see Figure 4).…”

Section: B Friend Relationship Matchingmentioning

confidence: 99%

Factor Graph Model Based User Profile Matching Across Social Networks

Wang

Zhang

et al. 2019

IEEE Access

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In modern society, it is common for people to be active in many different online social networks at once. As new social network services arise every year, it remains a great challenge to integrate social data. Discovering multiple profiles of a single person across different social networks is a precondition for integration, but it is still challenging due to the inconsistency and disruption of the accessible information among social media networks (SMNs). Many studies have made efforts on user's profiles, users' contents, and network structure to address this issue, but the issue of how to consider all these information in a unified model and tackle them simultaneously still remains challenging. Considering that identical users tend to have partial similar friend relationship structures in different SMNs, especially friendship SMNs, we deepen the analysis of ''friend'' relationships (mutual following connections) in different SMNs, and propose PIFGM (Pairwise Identical Factor Graph Model), a novel factor graph model-based model, to address this problem by considering both user attributes and friend relationships across networks. We also present a distributed learning algorithm to handle large-scale social networks. We evaluate the proposed model on two different data collections: SNS and SR. Our experimental results validate the effectiveness and efficiency of the proposed model. The proposed PIFGM significantly outperforms several alternative methods by up to approximately 10%∼20% in terms of F1 and precision on SNS and SR respectively. INDEX TERMS Cross-platform, user profile matching, factor graph model, friend relationship, PIFGM method.

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Section: B Friend Relationship Matchingmentioning

confidence: 99%

Factor Graph Model Based User Profile Matching Across Social Networks

Wang

Zhang

et al. 2019

IEEE Access

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“…As Newman states, ‘If vertex A is connected to vertex B and vertex B to vertex C, then there is a heightened probability that vertex A will also be connected to vertex C’ (‘Structure’, 178). Thus, if vertex A and vertex C connect, triadic closure occurs, and the resilience or sustainability of that part of the network increases (Huang et al, p. 3374). As this process continues, a large number of overlapping triangles begin to emerge, creating a strong cluster.…”

Section: Top 20 Letter Betweennessmentioning

confidence: 99%

Female Involvement, Membership, and Centrality: A Social Network Analysis of the Hartlib Circle

Bourke

2017

Literature Compass

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This article builds upon the pioneering work of Carol Pal, Lynette Hunter, Ruth Connolly and Michelle DiMeo by carrying out an extensive social network analysis of the Hartlib Papers. By adapting the methodology recently developed by Ruth Ahnert and Sebastian E. Ahnert to analyse Protestant letter networks memorialised in Foxe's Acts and Monuments, this article uses digital and visual tools to assess the importance of female involvement in the Hartlib Circle. It examines how integral female members were to the information flow of this network and the degree to which their positions in the network granted them influence over other members. Through this computational approach, this article also challenges the view that at its core the Hartlib Circle was made up of a group of male friends, arguing that Dorothy Moore Dury and Katherine Jones, Viscountess Ranelagh need to be recognised as integral elements of this network's core.

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“…In the past decades, knowledge graphs have played a pivotal role in many areas such as semantic search, question answering systems and so on. However, all of them have a need to improve their coverage of facts, for which link prediction is an effective strategy and has been paid much attention [5], [6], [7], [8]. Link prediction is a task to predict missing edges under the supervision of given part of knowledge graphs.…”

Section: Introductionmentioning

confidence: 99%

Link Prediction in Knowledge Graphs: A Hierarchy-Constrained Approach

Zhang

Jia

et al. 2022

IEEE Trans. Big Data

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Link prediction over a knowledge graph aims to predict the missing head entities h or tail entities t and missing relations r for a triple (h, r, t). Recent years have witnessed great advance of knowledge graph embedding based link prediction methods, which represent entities and relations as elements of a continuous vector space. Most methods learn the embedding vectors by optimizing a margin-based loss function, where the margin is used to separate negative and positive triples in the loss function. The loss function utilizes the general structures of knowledge graphs, e.g., the vector of r is the translation of the vector of h and t, and the vector of t should be the nearest neighbor of the vector of h + r. However, there are many particular structures, and can be employed to promote the performance of link prediction. One typical structure in knowledge graphs is hierarchical structure, which existing methods have much unexplored. We argue that the hierarchical structures also contain rich inference patterns, and can further enhance the link prediction performance. In this paper, we propose a hierarchy-constrained link prediction method, called hTransM, on the basis of the translation-based knowledge graph embedding methods. It can adaptively determine the optimal margin by detecting the single-step and multi-step hierarchical structures. Moreover, we prove the effectiveness of hTransM theoretically, and experiments over three benchmark datasets and two sub-tasks of link prediction demonstrate the superiority of hTransM.

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Triadic Closure Pattern Analysis and Prediction in Social Networks

Cited by 61 publications

References 37 publications

Factor Graph Model Based User Profile Matching Across Social Networks

Factor Graph Model Based User Profile Matching Across Social Networks

Female Involvement, Membership, and Centrality: A Social Network Analysis of the Hartlib Circle

Link Prediction in Knowledge Graphs: A Hierarchy-Constrained Approach

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