Map-Reduce based Link Prediction for Large Scale Social Network

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

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Social Network Analysis and Mining to Monitor and Identify Problems with Large-Scale Information and Communication Technology Interventions

PLoS ONE ◽

10.1371/journal.pone.0146220 ◽

2016 ◽

Vol 11 (1) ◽

pp. e0146220 ◽

Cited By ~ 5

Author(s):

Aleksandra do Socorro da Silva ◽

Silvana Rossy de Brito ◽

Nandamudi Lankalapalli Vijaykumar ◽

Cláudio Alex Jorge da Rocha ◽

Maurílio de Abreu Monteiro ◽

...

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Information And Communication Technology ◽

Communication Technology ◽

Large Scale ◽

Information And Communication

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Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

Advances in Multimedia ◽

10.1155/2018/2058670 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16

Author(s):

Jun Long ◽

Lei Zhu ◽

Zhan Yang ◽

Chengyuan Zhang ◽

Xinpan Yuan

Keyword(s):

Social Networks ◽

Social Network ◽

Large Scale ◽

Multimedia Data ◽

Attribute Information ◽

Novel Method ◽

Relationship Network ◽

Heterogeneous Social Networks ◽

The Relationship ◽

Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

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Anchor Link Prediction in Online Social Network Using Graph Embedding and Binary Classification

Computational Collective Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-030-63007-2_18 ◽

2020 ◽

pp. 229-240

Author(s):

Vang V. Le ◽

Tin T. Tran ◽

Phuong N. H. Pham ◽

Vaclav Snasel

Keyword(s):

Social Network ◽

Link Prediction ◽

Binary Classification ◽

Online Social Network ◽

Graph Embedding

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SageBook: Toward a cross-generational social network for the Jewish sages’ prosopography

Digital Scholarship in the Humanities ◽

10.1093/llc/fqy065 ◽

2018 ◽

Vol 34 (3) ◽

pp. 676-695

Author(s):

Maayan Zhitomirsky-Geffet ◽

Gila Prebor

Keyword(s):

Social Network ◽

Quantitative Analysis ◽

Large Scale ◽

Rabbinic Literature ◽

Jewish Law ◽

Related Data ◽

Jewish Thought ◽

Argumentation Analysis

Abstract In this research we devised and implemented a semi-automatic approach for building a SageBook–a cross-generational social network of the Jewish sages from the Rabbinic literature. The proposed methodology is based on a shallow argumentation analysis leading to detection of lexical–syntactic patterns which represent different relationships between the sages in the text. The method was successfully applied and evaluated on the corpus of the Mishna, the first written work of the Rabbinic Literature which provides the foundation to the Jewish law development. The constructed prosopographical database and the network generated from its data enable a large-scale quantitative analysis of the sages and their related data, and therefore might contribute to the research of the Talmudic literature and evolution of the Jewish thought throughout the two last millennia.

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