Link Prediction in Multi-modal Social Networks

Symeonidis, Panagiotis; Perentis, Christos

doi:10.1007/978-3-662-44845-8_10

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…Link prediction itself is a broadened area of research in social network analysis [4]. It has many applications in social and biological networks, such as the recommendation of new friends in Facebook [97] and drug-disease association prediction in biomedical networks [114]. For link prediction, a good embedding method should capture local information from the network and preserve it in the embedding space.…”

Section: Link Predictionsmentioning

confidence: 99%

A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods

Rahman¹,

Azad²

2021

Preprint

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Graph representation learning is a fast-growing field where one of the main objectives is to generate meaningful representations of graphs in lower-dimensional spaces. The learned embeddings have been successfully applied to perform various prediction tasks, such as link prediction, node classification, clustering, and visualization. The collective effort of the graph learning community has delivered hundreds of methods, but no single method excels under all evaluation metrics such as prediction accuracy, running time, scalability, etc. This survey aims to evaluate all major classes of graph embedding methods by considering algorithmic variations, parameter selections, scalability, hardware and software platforms, downstream ML tasks, and diverse datasets. We organized graph embedding techniques using a taxonomy that includes methods from manual feature engineering, matrix factorization, shallow neural networks, and deep graph convolutional networks. We evaluated these classes of algorithms for node classification, link prediction, clustering, and visualization tasks using widely used benchmark graphs. We designed our experiments on top of PyTorch Geometric and DGL libraries and run experiments on different multicore CPU and GPU platforms. We rigorously scrutinize the performance of embedding methods under various performance metrics and summarize the results. Thus, this paper may serve as a comparative guide to help users select methods that are most suitable for their tasks.

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Section: Link Predictionsmentioning

confidence: 99%

A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods

Rahman¹,

Azad²

2021

Preprint

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“…In this way, the user is not called to continuously take decisions concerning the disclosure of her/his personal information. In addition, the exploitation of usage patterns (i.e., logs from PDS tools) could enhance a privacy setting recommendation by employing an auxiliary information source [Symeonidis and Perentis 2014]. Concerning other available sources in a recommendation task, communication diversity and self-disclosure can be computed by using digital traces or by asking simple questions, respectively.…”

Section: Practical Implicationsmentioning

confidence: 99%

Anonymous or not? Understanding the Factors Affecting Personal Mobile Data Disclosure

Perentis¹,

Vescovi²,

Leonardi³

et al. 2017

Preprint

Self Cite

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The wide adoption of mobile devices and social media platforms have dramatically increased the collection and sharing of personal information. More and more frequently, users are called to take decisions concerning the disclosure of their personal information. In this study, we investigate the factors affecting users' choices toward the disclosure of their personal data, including not only their demographic and self-reported individual characteristics, but also their social interactions and their mobility patterns inferred from months of mobile phone data activity. We report the findings of a field-study conducted with a community of 63 subjects provided with (i) a smart-phone and (ii) a Personal Data Store (PDS) enabling them to control the disclosure of their data. We monitor the sharing behavior of our participants through the PDS, and evaluate the contribution of different factors affecting their disclosing choices of location and social interaction data. Our analysis shows that social interaction inferred by mobile phones is an important factor revealing willingness to share, regardless of the data type. In addition, we provide further insights on the individual traits relevant to the prediction of sharing behavior.CCS Concepts: •Human-centered computing → Ubiquitous and mobile computing systems and tools; •Security and privacy → Social aspects of security and privacy;

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“…In the wake of the development of signed networks, a growing interest goes to the trustability of a person by computing ranking 2 Wireless Communications and Mobile Computing nodes based on a criterion evaluating the trust worthiness. Some modified ranking algorithms [7,8] separate signed network into positive subgraph and negative subgraph and then compute the corresponding ranks using PageRank or Hits. Some important global information may be lost in those modified methods.…”

Section: Introductionmentioning

confidence: 99%

“…Some important global information may be lost in those modified methods. For example, node A has a friend B, and B has an enemy C. Methods [7] and [8] ignore the relationship between A and C. SRWR [9] could compute positive and negative scores of nodes in signed network, but these scores are from a personal view.…”

Section: Introductionmentioning

confidence: 99%

SignRank: A Novel Random Walking Based Ranking Algorithm in Signed Networks

Wei

Fang²,

et al. 2019

Wireless Communications and Mobile Computing

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Social networks have become an indispensable part of modern life. Signed networks, a class of social network with positive and negative edges, are becoming increasingly important. Many social networks have adopted the use of signed networks to model like (trust) or dislike (distrust) relationships. Consequently, how to rank nodes from positive and negative views has become an open issue of social network data mining. Traditional ranking algorithms usually separate the signed network into positive and negative graphs so as to rank positive and negative scores separately. However, much global information of signed network gets lost during the use of such methods, e.g., the influence of a friend’s enemy. In this paper, we propose a novel ranking algorithm that computes a positive score and a negative score for each node in a signed network. We introduce a random walking model for signed network which considers the walker has a negative or positive emotion. The steady state probability of the walker visiting a node with negative or positive emotion represents the positive score or negative score. In order to evaluate our algorithm, we use it to solve sign prediction problem, and the result shows that our algorithm has a higher prediction accuracy compared with some well-known ranking algorithms.

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Link Prediction in Multi-modal Social Networks

Cited by 9 publications

References 22 publications

A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods

A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods

Anonymous or not? Understanding the Factors Affecting Personal Mobile Data Disclosure

SignRank: A Novel Random Walking Based Ranking Algorithm in Signed Networks

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