Proceedings ArticleDOI
Link Prediction in Dynamic Social Networks: A Literature Review
Mohammad Marjan,Nazar Zaki,Elfadil A. Mohamed +2 more
- pp 200-207
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TLDR
The leading link prediction methods and techniques that network science has produced are categorized and compared, and features and evaluation metrics for each method are presented.Abstract:
Social network link prediction has gained significant attention and become a key research focus over the last two decades. The prediction of missing links in the current network and emerging or broken links in future networks is essential for the understanding of their evolutionary nature. Social networks are changing dynamically over time. Link inference in dynamic social networks is an extremely challenging process and few link prediction methods consider their evolving nature. The aim of this paper is to comprehensively review, analyze, discuss and evaluate state-of-the-art link prediction methods in dynamic social networks. The leading link prediction methods and techniques that network science has produced are categorized and compared. Features and evaluation metrics for each method are presented. Finally, some future directions and recommendations are provided.read more
Citations
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Journal ArticleDOI
Graph Deep Learning: State of the Art and Challenges
TL;DR: A survey of graph representation learning from the perspective of deep learning can be found in this article, where the authors identify four major challenges in graph deep learning: dynamic and evolving graphs, learning with edge signals and information, graph estimation, and the generalization of graph models.
Journal ArticleDOI
Review on Learning and Extracting Graph Features for Link Prediction
TL;DR: An extensive review of state-of-art methods and algorithms proposed on link prediction in complex networks is presented and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning- based methods.
Journal ArticleDOI
Review on Learning and Extracting Graph Features for Link Prediction
TL;DR: Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and knowledge graphs as discussed by the authors.
Journal ArticleDOI
Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features
Dayal Kumar Behera,Madhabananda Das,Subhra Swetanisha,Janmenjoy Nayak,S. Vimal,Bighnaraj Naik +5 more
TL;DR: In this paper, a supervised learning model (LPXGB) using XGBoost is proposed to consider the link prediction problem as a binary classification problem, which is used to represent the dataset suitable for machine learning.
Massive Scale Streaming Graphs: Evolving Network Analysis and Mining
TL;DR: This dissertation encapsulates contributions in three major perspectives: Analysis, Sampling, and Mining of streaming networks, which proposes algorithms that comply with single-pass and limited memory for processing, and presents dynamic sampling on evolving networks.
References
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Journal IssueDOI
The link-prediction problem for social networks
David Liben-Nowell,Jon Kleinberg +1 more
TL;DR: Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.
Journal ArticleDOI
Link prediction in complex networks: A survey
TL;DR: Recent progress about link prediction algorithms is summarized, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods.
Journal ArticleDOI
Link prediction in social networks: the state-of-the-art
TL;DR: A systematical category for link prediction techniques and problems is presented, and some future challenges of the link prediction in social networks are discussed.
Journal ArticleDOI
Temporal Link Prediction Using Matrix and Tensor Factorizations
TL;DR: In this article, a weight-based method for collapsing multi-year data into a single matrix was proposed, which can be extended to bipartite graphs and moreover approximated in a scalable way using a truncated singular value decomposition.
Journal ArticleDOI
Temporal Link Prediction using Matrix and Tensor Factorizations
TL;DR: This article considers bipartite graphs that evolve over time and considers matrix- and tensor-based methods for predicting future links and shows that Tensor- based techniques are particularly effective for temporal data with varying periodic patterns.