scholarly journals A Potential Information Capacity Index for Link Prediction of Complex Networks Based on the Cannikin Law

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 863 ◽  
Author(s):  
Xing Li ◽  
Shuxin Liu ◽  
Hongchang Chen ◽  
Kai Wang
Keyword(s):  
Complex Networks ◽  
Information Transmission ◽  
Link Prediction ◽  
Information Capacity ◽  
Prediction Methods ◽  
Transmission Process ◽  
Capacity Index ◽  
Definition Of ◽  
Definition Of Information ◽  
Transmission Capability

Recently, a number of similarity-based methods have been proposed for link prediction of complex networks. Among these indices, the resource-allocation-based prediction methods perform very well considering the amount of resources in the information transmission process between nodes. However, they ignore the information channels and their information capacity in information transmission process between two endpoints. Motivated by the Cannikin Law, the definition of information capacity is proposed to quantify the information transmission capability between any two nodes. Then, based on the information capacity, a potential information capacity (PIC) index is proposed for link prediction. Empirical study on 15 datasets has shown that the PIC index we proposed can achieve a good performance, compared with eight mainstream baselines.

scholarly journals A novel similarity measure for missing link prediction in social networks

2020 ◽  
Vol 19 (2) ◽  
pp. 1071
Author(s):  
Gogulamudi Naga Chandrika ◽  
E. Srinivasa Reddy
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Similarity Measure ◽  
Network Theory ◽  
Link Prediction ◽  
Prediction Methods ◽  
Missing Link ◽  
The Social ◽  
Hidden Patterns ◽  
Over Time

<p><span>Social Networks progress over time by the addition of new nodes and links, form associations with one community to the other community. Over a few decades, the fast expansion of Social Networks has attracted many researchers to pay more attention towards complex networks, the collection of social data, understand the social behaviors of complex networks and predict future conflicts. Thus, Link prediction is imperative to do research with social networks and network theory. The objective of this research is to find the hidden patterns and uncovered missing links over complex networks. Here, we developed a new similarity measure to predict missing links over social networks. The new method is computed on common neighbors with node-to-node distance to get better accuracy of missing link prediction. </span><span>We tested the proposed measure on a variety of real-world linked datasets which are formed from various linked social networks. The proposed approach performance is compared with contemporary link prediction methods. Our measure makes very effective and intuitive in predicting disappeared links in linked social networks.</span></p>

Similarity indices based on link weight assignment for link prediction of unweighted complex networks

2017 ◽  
Vol 31 (02) ◽  
pp. 1650254 ◽  
Author(s):  
Shuxin Liu ◽  
Xinsheng Ji ◽  
Caixia Liu ◽  
Yi Bai
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Prediction Methods ◽  
Close Attention ◽  
Common Neighbor ◽  
Link Weight ◽  
Similarity Indices ◽  
Clustered Networks ◽  
Local Path

Many link prediction methods have been proposed for predicting the likelihood that a link exists between two nodes in complex networks. Among these methods, similarity indices are receiving close attention. Most similarity-based methods assume that the contribution of links with different topological structures is the same in the similarity calculations. This paper proposes a local weighted method, which weights the strength of connection between each pair of nodes. Based on the local weighted method, six local weighted similarity indices extended from unweighted similarity indices (including Common Neighbor (CN), Adamic-Adar (AA), Resource Allocation (RA), Salton, Jaccard and Local Path (LP) index) are proposed. Empirical study has shown that the local weighted method can significantly improve the prediction accuracy of these unweighted similarity indices and that in sparse and weakly clustered networks, the indices perform even better.

Prediction of missing links and reconstruction of complex networks

2016 ◽  
Vol 27 (10) ◽  
pp. 1650120 ◽  
Author(s):  
Cheng-Jun Zhang ◽  
An Zeng
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
High Performance ◽  
Point Of View ◽  
Prediction Methods ◽  
Online Systems ◽  
Network Properties ◽  
High Prediction ◽  
Simple Link

Predicting missing links in complex networks is of great significance from both theoretical and practical point of view, which not only helps us understand the evolution of real systems but also relates to many applications in social, biological and online systems. In this paper, we study the features of different simple link prediction methods, revealing that they may lead to the distortion of networks’ structural and dynamical properties. Moreover, we find that high prediction accuracy is not definitely corresponding to a high performance in preserving the network properties when using link prediction methods to reconstruct networks. Our work highlights the importance of considering the feedback effect of the link prediction methods on network properties when designing the algorithms.

scholarly journals Seven-Layer Model in Complex Networks Link Prediction: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6560
Author(s):  
Hui Wang ◽  
Zichun Le
Keyword(s):  
Complex Networks ◽  
Supervised Learning ◽  
Link Prediction ◽  
Complex Analysis ◽  
Auxiliary Information ◽  
Prediction Method ◽  
Prediction Methods ◽  
Layer Model ◽  
The Future ◽  
High Level

Link prediction is the most basic and essential problem in complex networks. This study analyzes the observed topological, time, attributive, label, weight, directional, and symbolic features and auxiliary information to find the lack of connection and predict the future possible connection. For discussion and analysis of the evolution of the network, the network model is of great significance. In the past two decades, link prediction has attracted extensive attention from experts in various fields, who have published numerous high-level papers, but few combine interdisciplinary characteristics. This survey analyzes and discusses the existing link prediction methods. The idea of stratification is introduced into the classification system of link prediction for the first time and proposes the design idea of a seven-layer model, namely the network, metadata, feature classification, selection input, processing, selection, and output layers. Among them, the processing layer divides link prediction methods into similarity-based, probabilistic, likelihood, supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning methods. The input features, evaluation metrics, complex analysis, experimental comparisons, relative merits, common dataset and open-source implementations for each link prediction method are then discussed in detail. Through analysis and comparison, we found that the link prediction method based on graph structure features has better prediction performance. Finally, the future development direction of link prediction in complex networks is discussed.

Link Prediction in Complex Networks

2020 ◽  
pp. 1196-1236
Author(s):  
Manisha Pujari ◽  
Rushed Kanawati
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Class Imbalance ◽  
Rank Aggregation ◽  
General Description ◽  
Multiplex Network ◽  
Community Information ◽  
Target Network ◽  
Definition Of ◽  
Prediction Approach

This chapter presents the problem of link prediction in complex networks. It provides general description, formal definition of the problem and applications. It gives a state-of-art of various existing link prediction approaches concentrating more on topological approaches. It presents the main challenges of link prediction task in real networks. There is description of our new link prediction approach based on supervised rank aggregation and our attempts to deal with two of the challenges to improve the prediction results. One approach is to extend the set of attributes describing an example (pair of nodes) calculated in a multiplex network that includes the target network. Multiplex networks have a layered structure, each layer having different kinds of links between same sets of nodes. The second way is to use community information for sampling of examples to deal with the problem of class imbalance. All experiments have been conducted on real networks extracted from well-known DBLP bibliographic database.

Link Prediction in Complex Networks

2016 ◽  
pp. 58-97 ◽  
Author(s):  
Manisha Pujari ◽  
Rushed Kanawati
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Class Imbalance ◽  
Rank Aggregation ◽  
General Description ◽  
Multiplex Network ◽  
Community Information ◽  
Target Network ◽  
Definition Of ◽  
Prediction Approach

This chapter presents the problem of link prediction in complex networks. It provides general description, formal definition of the problem and applications. It gives a state-of-art of various existing link prediction approaches concentrating more on topological approaches. It presents the main challenges of link prediction task in real networks. There is description of our new link prediction approach based on supervised rank aggregation and our attempts to deal with two of the challenges to improve the prediction results. One approach is to extend the set of attributes describing an example (pair of nodes) calculated in a multiplex network that includes the target network. Multiplex networks have a layered structure, each layer having different kinds of links between same sets of nodes. The second way is to use community information for sampling of examples to deal with the problem of class imbalance. All experiments have been conducted on real networks extracted from well-known DBLP bibliographic database.

Definition of Information Core for Documents Classification

2018 ◽  
Vol 50 (4) ◽  
pp. 25-34 ◽  
Author(s):  
Eduard A. Manziuk ◽  
Alexander V. Barmak ◽  
Yuriy V. Krak ◽  
Veda S. Kasianiuk
Keyword(s):  
Definition Of ◽  
Definition Of Information

scholarly journals Chained graphs and some applications

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Anna Concas ◽  
Lothar Reichel ◽  
Giuseppe Rodriguez ◽  
Yunzi Zhang
Keyword(s):  
Information Transmission ◽  
City Planning ◽  
Structural Information ◽  
Closeness Centrality ◽  
Planning Algorithms ◽  
Definition Of

AbstractThis paper introduces the notions of chained and semi-chained graphs. The chain of a graph, when existent, refines the notion of bipartivity and conveys important structural information. Also the notion of a center vertex $$v_c$$ v c is introduced. It is a vertex, whose sum of p powers of distances to all other vertices in the graph is minimal, where the distance between a pair of vertices $$\{v_c,v\}$$ { v c , v } is measured by the minimal number of edges that have to be traversed to go from $$v_c$$ v c to v. This concept extends the definition of closeness centrality. Applications in which the center node is important include information transmission and city planning. Algorithms for the identification of approximate central nodes are provided and computed examples are presented.

scholarly journals Effective Data Transmission and Control Based on Social Communication in Social Opportunistic Complex Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20 ◽  
Author(s):  
Weiyu Yang ◽  
Jia Wu ◽  
Jingwen Luo
Keyword(s):  
Complex Networks ◽  
Information Transmission ◽  
Interference Cancellation ◽  
Propagation Delay ◽  
Transmission Efficiency ◽  
Successive Interference Cancellation ◽  
Time Slice ◽  
Signal Interference ◽  
Time Signal ◽  
Interference Avoidance

In opportunistic complex networks, information transmission between nodes is inevitable through broadcast. The purpose of broadcasting is to distribute data from source nodes to all nodes in the network. In opportunistic complex networks, it is mainly used for routing discovery and releasing important notifications. However, when a large number of nodes in the opportunistic complex networks are transmitting information at the same time, signal interference will inevitably occur. Therefore, we propose a low-latency broadcast algorithm for opportunistic complex networks based on successive interference cancellation techniques to improve propagation delay. With this kind of algorithm, when the social network is broadcasting, this algorithm analyzes whether the conditions for successive interference cancellation are satisfied between the broadcast links on the assigned transmission time slice. If the conditions are met, they are scheduled at the same time slice, and interference avoidance scheduling is performed when conditions are not met. Through comparison experiments with other classic algorithms of opportunistic complex networks, this method has outstanding performance in reducing energy consumption and improving information transmission efficiency.

Definition of information and entropy in the absence of noise

2020 ◽  
pp. 1-33
Author(s):  
Roman V. Belavkin ◽  
Panos M. Pardalos ◽  
Jose C. Principe ◽  
Ruslan L. Stratonovich
Keyword(s):  
Definition Of ◽  
Definition Of Information
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