State of the Art Applications of Social Network 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.

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The state of the art development of AHP (1979–2017): a literature review with a social network analysis

International Journal of Production Research ◽

10.1080/00207543.2017.1334976 ◽

2017 ◽

Vol 55 (22) ◽

pp. 6653-6675 ◽

Cited By ~ 66

Author(s):

Ali Emrouznejad ◽

Marianna Marra

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Literature Review ◽

State Of The Art ◽

The State

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Mining Largest Maximal Quasi-Cliques

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3446637 ◽

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.

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

Scientific Reports ◽

10.1038/s41598-020-76799-4 ◽

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.

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