A Community-Based Approach for Link Prediction in Signed Social Networks

Shahriary, Saeed Reza; Shahriari, Mohsen; Noor, Rafidah Md

doi:10.1155/2015/602690

Cited by 11 publications

(2 citation statements)

References 35 publications

(65 reference statements)

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“…In the same year, S.R. Shahriary et al [24] used a community discovery strategy for ranking algorithms in signed graphs as a contribution.…”

Section: Related Workmentioning

confidence: 99%

A Novel PSO based Intelligence Link Prediction Algorithm in Real World Networks

Choudhury

Acharjee

2022

Preprint

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Link prediction is a critical challenge to analyse social networks with applications in areas such as information retrieval, bioinformatics, e-commerce etc. To overcome the challenges of either predicting the missing links or finding the future connections, a variety of approaches have been presented so far. Similarity-based approaches, which evaluate the network structure and use the number of shared neighbours between two nodes as a key criterion to determine structural similarity , are the most efficient among these methods. In this paper, we have proposed a novel link prediction algorithm based on Particle Swarm Optimization (PSO) and implemented it on different real world datasets. Our proposed approach achieved 72.75% accuracy in Zachary karate Club, 75.26% accuracy in Bottlenose Dolphin Network, 80.35% accuracy in College Football Network, and 78.36% accuracy in Kreb’s book Network which beat the performances of traditional algorithms.

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“…In the same year, S.R. Shahriary et al [24] used a community discovery strategy for ranking algorithms in signed graphs as a contribution.…”

Section: Related Workmentioning

confidence: 99%

A Novel PSO based Intelligence Link Prediction Algorithm in Real World Networks

Choudhury

Acharjee

2022

Preprint

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“…Other algorithms exist which are variations to HITS and PageRank like SALSA [4] and ExpertRank [8]. [24] applied community-aware ranking algorithm on the problem of sign prediction. [6,26] used cluster-based collaborative filtering and spectral clustering to the case of link prediction.…”

Section: Ocd and Ranking Algorithms For Expert Identification In Qafsmentioning

confidence: 99%

Community-aware ranking algorithms for expert identification in question-answer forums

Shahriari

Parekodi

Klamma

2015

Proceedings of the 15th International Conference on Knowledge Technologies and Data-Driven Business

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Question-Answer forums (QAF) are significant platforms for disseminating informal information and play important role in problem solving and learning. Expert identification still has some limitations and link analysis methods do not consider community dimension. In this paper an authority analysis approach for identifying experts is proposed. This approach combines overlapping community detection (OCD) algorithms with ranking methods to compute the nodes' expertise level in QAFs. Firstly, graph resulting from a specific search query is computed and an OCD algorithm is applied on it. After identifying clusters of nodes, we change updating rules of original Hyperlink-Induced Topic Search (HITS) and PageRank to take the effect of intra cluster links and extra cluster connections. People whom are intra or overlapping to a community possess higher vision about context of the community than nodes which are outside. We experimented the proposed overlapping community-aware ranking algorithms and compared them with baseline approaches on online forums. Results indicate that OCD improves expert identification accuracy and relevancy.

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Link prediction on signed social networks based on latent space mapping

Chen

et al. 2018

Appl Intell

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A Community-Based Approach for Link Prediction in Signed Social Networks

Cited by 11 publications

References 35 publications

A Novel PSO based Intelligence Link Prediction Algorithm in Real World Networks

A Novel PSO based Intelligence Link Prediction Algorithm in Real World Networks

Community-aware ranking algorithms for expert identification in question-answer forums

Link prediction on signed social networks based on latent space mapping

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