scholarly journals Similarity-based link prediction in social networks using latent relationships between the users

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ahmad Zareie ◽  
Rizos Sakellariou
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Interactions ◽  
Link Prediction ◽  
State Of The Art ◽  
Structural Similarity ◽  
Good Efficiency ◽  
Comprehensive Information ◽  
High Structural Similarity

AbstractSocial network analysis has recently attracted lots of attention among researchers due to its wide applicability in capturing social interactions. Link prediction, related to the likelihood of having a link between two nodes of the network that are not connected, is a key problem in social network analysis. Many methods have been proposed to solve the problem. Among these methods, similarity-based methods exhibit good efficiency by considering the network structure and using as a fundamental criterion the number of common neighbours between two nodes to establish structural similarity. High structural similarity may suggest that a link between two nodes is likely to appear. However, as shown in the paper, the number of common neighbours may not be always sufficient to provide comprehensive information about structural similarity between a pair of nodes. To address this, a neighbourhood vector is first specified for each node. Then, a novel measure is proposed to determine the similarity of each pair of nodes based on the number of common neighbours and correlation between the neighbourhood vectors of the nodes Experimental results, on a range of different real-world networks, suggest that the proposed method results in higher accuracy than other state-of-the-art similarity-based methods for link prediction.

An Experimental Evaluation of Link Prediction for Movie Suggestions Using Social Media Content

2018 ◽  
pp. 203-230
Author(s):  
Anu Taneja ◽  
Bhawna Gupta ◽  
Anuja Arora
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Networks ◽  
Experimental Evaluation ◽  
Link Prediction ◽  
Common Neighbor ◽  
Research Directions ◽  
Definition Of

The enormous growth and dynamic nature of online social networks have emerged to new research directions that examine the social network analysis mechanisms. In this chapter, the authors have explored a novel technique of recommendation for social media and used well known social network analysis (SNA) mechanisms-link prediction. The initial impetus of this chapter is to provide general description, formal definition of the problem, its applications, state-of-art of various link prediction approaches in social media networks. Further, an experimental evaluation has been made to inspect the role of link prediction in real environment by employing basic common neighbor link prediction approach on IMDb data. To improve performance, weighted common neighbor link prediction (WCNLP) approach has been proposed. This exploits the prediction features to predict new links among users of IMDb. The evaluation shows how the inclusion of weight among the nodes offers high link prediction performance and opens further research directions.

Incorporating Trust Relation with PMF to Enhance Social Network Recommendation Performance

2016 ◽  
Vol 30 (06) ◽  
pp. 1659016 ◽  
Author(s):  
Hui Li ◽  
Xiao-Ping Ma ◽  
Jun Shi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Matrix Factorization ◽  
Web Applications ◽  
State Of The Art ◽  
Large Datasets ◽  
Trust Relation ◽  
The Empirical Analysis

In view of the exponential growth of information generated by social networks, social network analysis and recommendation have become important for many web applications. This paper examines the problem of social collaborative filtering to recommend items of interest to users in a social network setting. Many social networks capture the relationships among the nodes by using trust scores to label the edges. The bias of a node denotes its propensity to trust/mistrust its neighbors and is closely related to truthfulness. It is based on the idea that the recommendation of a highly biased node should be removed. In this paper, we propose a model-based approach for recommendation employing matrix factorization after removing the bias nodes from each link, which naturally fuses the users’ tastes and their trusted friends’ favors together. The empirical analysis on real large datasets demonstrate that our approaches outperform other state-of-the-art methods.

scholarly journals Joint Link Prediction and Network Alignment via Cross-graph Embedding

2019 ◽  
Author(s):  
Xingbo Du ◽  
Junchi Yan ◽  
Hongyuan Zha
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Link Prediction ◽  
Graph Embedding ◽  
Network Alignment ◽  
Initial Vertex ◽  
Information Propagation ◽  
Graph Node ◽  
Embedding Technique

Link prediction and network alignment are two important problems in social network analysis and other network related applications. Considerable efforts have been devoted to these two problems while often in an independent way to each other. In this paper we argue that these two tasks are relevant and present a joint link prediction and network alignment framework, whereby a novel cross-graph node embedding technique is devised to allow for information propagation. Our approach can either work with a few initial vertex correspondence as seeds, or from scratch. By extensive experiments on public benchmark, we show that link prediction and network alignment can benefit to each other especially for improving the recall for both tasks.

scholarly journals Taxonomy of Link Prediction for Social Network Analysis: A Review

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 183470-183487
Author(s):  
Herman Yuliansyah ◽  
Zulaiha Ali Othman ◽  
Azuraliza Abu Bakar
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Link Prediction

Mining Largest Maximal Quasi-Cliques

2021 ◽  
Vol 15 (5) ◽  
pp. 1-21
Author(s):  
Seyed-Vahid Sanei-Mehri ◽  
Apurba Das ◽  
Hooman Hashemi ◽  
Srikanta Tirthapura
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Heuristic Algorithm ◽  
State Of The Art ◽  
Experimental Results ◽  
Np Hard ◽  
Challenging Problem ◽  
Current State ◽  
Clique Enumeration

Quasi-cliques are dense incomplete subgraphs of a graph that generalize the notion of cliques. Enumerating quasi-cliques from a graph is a robust way to detect densely connected structures with applications in bioinformatics and social network analysis. However, enumerating quasi-cliques in a graph is a challenging problem, even harder than the problem of enumerating cliques. We consider the enumeration of top- k degree-based quasi-cliques and make the following contributions: (1) we show that even the problem of detecting whether a given quasi-clique is maximal (i.e., not contained within another quasi-clique) is NP-hard. (2) We present a novel heuristic algorithm K ernel QC to enumerate the k largest quasi-cliques in a graph. Our method is based on identifying kernels of extremely dense subgraphs within a graph, followed by growing subgraphs around these kernels, to arrive at quasi-cliques with the required densities. (3) Experimental results show that our algorithm accurately enumerates quasi-cliques from a graph, is much faster than current state-of-the-art methods for quasi-clique enumeration (often more than three orders of magnitude faster), and can scale to larger graphs than current methods.

scholarly journals Prospects for the Analysis and Reduction of Damaging Behaviour in Group-Housed Livestock, With Application to Pig Breeding

2020 ◽  
Vol 11 ◽  
Author(s):  
Laurianne Canario ◽  
Piter Bijma ◽  
Ingrid David ◽  
Irene Camerlink ◽  
Alexandre Martin ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Missing Data ◽  
Network Analysis ◽  
Social Interactions ◽  
Emotional Distress ◽  
Social Groups ◽  
Genetic Selection ◽  
Accurate Estimation ◽  
Capture Recapture

Innovations in the breeding and management of pigs are needed to improve the performance and welfare of animals raised in social groups, and in particular to minimise biting and damage to group mates. Depending on the context, social interactions between pigs can be frequent or infrequent, aggressive, or non-aggressive. Injuries or emotional distress may follow. The behaviours leading to damage to conspecifics include progeny savaging, tail, ear or vulva biting, and excessive aggression. In combination with changes in husbandry practices designed to improve living conditions, refined methods of genetic selection may be a solution reducing these behaviours. Knowledge gaps relating to lack of data and limits in statistical analyses have been identified. The originality of this paper lies in its proposal of several statistical methods for common use in analysing and predicting unwanted behaviours, and for genetic use in the breeding context. We focus on models of interaction reflecting the identity and behaviour of group mates which can be applied directly to damaging traits, social network analysis to define new and more integrative traits, and capture-recapture analysis to replace missing data by estimating the probability of behaviours. We provide the rationale for each method and suggest they should be combined for a more accurate estimation of the variation underlying damaging behaviours.

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