Discovering spurious links in multiplex networks based on interlayer relevance

2019 ◽  
Vol 7 (5) ◽  
pp. 641-658 ◽  
Author(s):  
Zeynab Samei ◽  
Mahdi Jalili
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Link Prediction ◽  
Large Scale ◽  
Transportation Networks ◽  
Similarity Index ◽  
Similarity Measures ◽  
Computation Complexity ◽  
Multiplex Networks ◽  
Large Scale Networks

Abstract Many real-world complex systems can be better modelled as multiplex networks, where the same individuals develop connections in multiple layers. Examples include social networks between individuals on multiple social networking platforms, and transportation networks between cities based on air, rail and road networks. Accurately predicting spurious links in multiplex networks is a challenging issue. In this article, we show that one can effectively use interlayer information to build an algorithm for spurious link prediction. We propose a similarity index that combines intralayer similarity with interlayer relevance for the link prediction purpose. The proposed similarity index is used to rank the node pairs, and identify those that are likely to be spurious. Our experimental results show that the proposed metric is much more accurate than intralayer similarity measures in correctly predicting the spurious links. The proposed method is an unsupervised method and has low computation complexity, and thus can be effectively applied for spurious link prediction in large-scale networks.

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

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.

Link prediction via layer relevance of multiplex networks

2017 ◽  
Vol 28 (08) ◽  
pp. 1750101 ◽  
Author(s):  
Yabing Yao ◽  
Ruisheng Zhang ◽  
Fan Yang ◽  
Yongna Yuan ◽  
Qingshuang Sun ◽  
...  
Keyword(s):  
Structural Properties ◽  
Link Prediction ◽  
Structural Information ◽  
Similarity Index ◽  
Single Layer ◽  
Structural Features ◽  
Prediction Performance ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Node Similarity

In complex networks, the existing link prediction methods primarily focus on the internal structural information derived from single-layer networks. However, the role of interlayer information is hardly recognized in multiplex networks, which provide more diverse structural features than single-layer networks. Actually, the structural properties and functions of one layer can affect that of other layers in multiplex networks. In this paper, the effect of interlayer structural properties on the link prediction performance is investigated in multiplex networks. By utilizing the intralayer and interlayer information, we propose a novel “Node Similarity Index” based on “Layer Relevance” (NSILR) of multiplex network for link prediction. The performance of NSILR index is validated on each layer of seven multiplex networks in real-world systems. Experimental results show that the NSILR index can significantly improve the prediction performance compared with the traditional methods, which only consider the intralayer information. Furthermore, the more relevant the layers are, the higher the performance is enhanced.

The Role of Social Networking in Civilizational Development

2013 ◽  
pp. 1193-1217
Author(s):  
Andrew Targowski
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
21St Century ◽  
Large Scale ◽  
Rapid Development ◽  
World Government ◽  
Electronic Culture ◽  
The Impact ◽  
Further Development

This study defines the nature, scope, pace, and consequences of the rapid development of social networking in the 21st century. The impact of INFOCO systems upon civilizational development is investigated and predicted for the future. Such concepts as Global Virtual Society, Global Virtual Nation, Virtual Nation, Virtual Users, National Virtual Citizens, and Global Virtual Citizens are defined. Their electronic culture is defined, too. Finally some recommendations for further research are provided. In particular, the question of whether the concept of one world government is good or bad for mankind should be answered soon, in order to properly steer the further development of large-scale social networks.

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

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Saeed Reza Shahriary ◽  
Mohsen Shahriari ◽  
Rafidah MD Noor
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Link Prediction ◽  
Large Scale ◽  
Prediction Problem ◽  
Signed Graphs ◽  
Community Based ◽  
Ranking Algorithms ◽  
Detection Approach ◽  
Signed Social Networks

In signed social networks, relationships among nodes are of the types positive (friendship) and negative (hostility). One absorbing issue in signed social networks is predicting sign of edges among people who are members of these networks. Other than edge sign prediction, one can define importance of people or nodes in networks via ranking algorithms. There exist few ranking algorithms for signed graphs; also few studies have shown role of ranking in link prediction problem. Hence, we were motivated to investigate ranking algorithms availed for signed graphs and their effect on sign prediction problem. This paper makes the contribution of using community detection approach for ranking algorithms in signed graphs. Therefore, community detection which is another active area of research in social networks is also investigated in this paper. Community detection algorithms try to find groups of nodes in which they share common properties like similarity. We were able to devise three community-based ranking algorithms which are suitable for signed graphs, and also we evaluated these ranking algorithms via sign prediction problem. These ranking algorithms were tested on three large-scale datasets: Epinions, Slashdot, and Wikipedia. We indicated that, in some cases, these ranking algorithms outperform previous works because their prediction accuracies are better.

scholarly journals Identifying and quantifying potential super-spreaders in social networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Dayong Zhang ◽  
Yang Wang ◽  
Zhaoxin Zhang
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Time Complexity ◽  
Large Scale ◽  
Network Connectivity ◽  
Sir Model ◽  
The Other ◽  
Nearest Neighbours ◽  
Large Scale Networks ◽  
Accuracy And Stability

Abstract Quantifying the nodal spreading abilities and identifying the potential influential spreaders has been one of the most engaging topics recently, which is essential and beneficial to facilitate information flow and ensure the stabilization operations of social networks. However, most of the existing algorithms just consider a fundamental quantification through combining a certain attribute of the nodes to measure the nodes’ importance. Moreover, reaching a balance between the accuracy and the simplicity of these algorithms is difficult. In order to accurately identify the potential super-spreaders, the CumulativeRank algorithm is proposed in the present study. This algorithm combines the local and global performances of nodes for measuring the nodal spreading abilities. In local performances, the proposed algorithm considers both the direct influence from the node’s neighbourhoods and the indirect influence from the nearest and the next nearest neighbours. On the other hand, in the global performances, the concept of the tenacity is introduced to assess the node’s prominent position in maintaining the network connectivity. Extensive experiments carried out with the Susceptible-Infected-Recovered (SIR) model on real-world social networks demonstrate the accuracy and stability of the proposed algorithm. Furthermore, the comparison of the proposed algorithm with the existing well-known algorithms shows that the proposed algorithm has lower time complexity and can be applicable to large-scale networks.

SIMILARITY INDEX BASED ON THE INFORMATION OF NEIGHBOR NODES FOR LINK PREDICTION OF COMPLEX NETWORK

2013 ◽  
Vol 27 (06) ◽  
pp. 1350039 ◽  
Author(s):  
JING WANG ◽  
LILI RONG
Keyword(s):  
Link Prediction ◽  
High Efficiency ◽  
Similarity Index ◽  
Similarity Measures ◽  
Nearest Neighbors ◽  
Clustering Coefficient ◽  
Local Similarity ◽  
Common Neighbor ◽  
Similarity Indices ◽  
Node Similarity

Link prediction in complex networks has attracted much attention recently. Many local similarity measures based on the measurements of node similarity have been proposed. Among these local similarity indices, the neighborhood-based indices Common Neighbors (CN), Adamic-Adar (AA) and Resource Allocation (RA) index perform best. It is found that the node similarity indices required only information on the nearest neighbors are assigned high scores and have very low computational complexity. In this paper, a new index based on the contribution of common neighbor nodes to edges is proposed and shown to have competitively good or even better prediction than other neighborhood-based indices especially for the network with low clustering coefficient with its high efficiency and simplicity.

The Role of Social Networking in Civilizational Development

2012 ◽  
pp. 226-251
Author(s):  
Andrew Targowski
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
21St Century ◽  
Large Scale ◽  
Rapid Development ◽  
World Government ◽  
Electronic Culture ◽  
The Impact ◽  
Further Development

This study defines the nature, scope, pace, and consequences of the rapid development of social networking in the 21st century. The impact of INFOCO systems upon civilizational development is investigated and predicted for the future. Such concepts as Global Virtual Society, Global Virtual Nation, Virtual Nation, Virtual Users, National Virtual Citizens, and Global Virtual Citizens are defined. Their electronic culture is defined, too. Finally some recommendations for further research are provided. In particular, the question of whether the concept of one world government is good or bad for mankind should be answered soon, in order to properly steer the further development of large-scale social networks.

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