Proceedings ArticleDOI
Prediction of Binary Labels for Edges in Signed Networks: A Random-Walk Based Approach
Mukul Gupta,Rajhans Mishra +1 more
- pp 1-4
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TLDR
A semi-supervised approach is proposed that uses the concept of random-walk for prediction of binary labels for edges in undirected and unweighted networks.Abstract:
Mining of signed networks where the links/edges between nodes have a positive or negative sign/label, is getting the attention of researchers and practitioners due to its wide realworld applicability in various domains. Label prediction for nodes in a network is a well-known and explored problem. However, the prediction of labels for edges in a network is relatively less explored, and very challenging and interesting problem. In this paper, we consider the problem of binary label prediction for edges in undirected and unweighted networks. The prediction of binary labels has a number of applications in realworld like friend/foe prediction, recommendation, trust/distrust prediction in social networks, and categorization. In this work, a semi-supervised approach is proposed that uses the concept of random-walk for prediction of binary labels for edges. In this paper, we demonstrate the viability and the effectiveness of the proposed approach using a real-world network.read more
Citations
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Proceedings ArticleDOI
LoCEC: Local Community-based Edge Classification in Large Online Social Networks
TL;DR: Li et al. as discussed by the authors proposed a Local Community-based Edge Classification (LoCEC) framework that classifies user relationships in a social network into real-world social connection types, which enforces a three-phase processing, namely local community detection, community classification and relationship classification.
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LoCEC: Local Community-based Edge Classification in Large Online Social Networks
TL;DR: This paper presents an in-depth analysis of WeChat relationships to identify the major challenges for the relationship classification task and proposes a Local Community-based Edge Classification (LoCEC) framework that classifies user relationships in a social network into real-world social connection types.
References
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Proceedings ArticleDOI
Predicting positive and negative links in online social networks
TL;DR: These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology and suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.
Posted Content
Signed Networks in Social Media
TL;DR: In this article, the authors studied how the interplay between positive and negative relationships affects the structure of on-line social networks and found that the classical theory of structural balance tends to capture certain common patterns of interaction, but that it is also at odds with some of the fundamental phenomena they observe.
Proceedings ArticleDOI
Signed networks in social media
TL;DR: This work provides one of the first large-scale evaluations of theories of signed networks using on-line datasets, as well as providing a perspective for reasoning about social media sites.
Proceedings ArticleDOI
Learning from labeled and unlabeled data on a directed graph
TL;DR: A general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered, which generalizes the spectral clustering approach for undirected graphs.
Book ChapterDOI
An Introduction to Social Network Data Analytics
TL;DR: This book provides a data-centric view of online social networks; a topic which has been missing from much of the literature.
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