1
|
Liu S, Liu Y, Yang C, Deng L. Relative Entropy of Distance Distribution Based Similarity Measure of Nodes in Weighted Graph Data. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1154. [PMID: 36010818 PMCID: PMC9407273 DOI: 10.3390/e24081154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/10/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
Many similarity measure algorithms of nodes in weighted graph data have been proposed by employing the degree of nodes in recent years. Despite these algorithms obtaining great results, there may be still some limitations. For instance, the strength of nodes is ignored. Aiming at this issue, the relative entropy of the distance distribution based similarity measure of nodes is proposed in this paper. At first, the structural weights of nodes are given by integrating their degree and strength. Next, the distance between any two nodes is calculated with the help of their structural weights and the Euclidean distance formula to further obtain the distance distribution of each node. After that, the probability distribution of nodes is constructed by normalizing their distance distributions. Thus, the relative entropy can be applied to measure the difference between the probability distributions of the top d important nodes and all nodes in graph data. Finally, the similarity of two nodes can be measured in terms of this above-mentioned difference calculated by relative entropy. Experimental results demonstrate that the algorithm proposed by considering the strength of node in the relative entropy has great advantages in the most similar node mining and link prediction.
Collapse
|
2
|
Li L, Wen Y, Bai S, Liu P. Link prediction in weighted networks via motif predictor. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
3
|
Jafari SH, Abdolhosseini-Qomi AM, Asadpour M, Rahgozar M, Yazdani N. An information theoretic approach to link prediction in multiplex networks. Sci Rep 2021; 11:13242. [PMID: 34168194 PMCID: PMC8225891 DOI: 10.1038/s41598-021-92427-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 06/10/2021] [Indexed: 11/09/2022] Open
Abstract
The 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.
Collapse
Affiliation(s)
- Seyed Hossein Jafari
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | | | - Masoud Asadpour
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Maseud Rahgozar
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Naser Yazdani
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| |
Collapse
|
4
|
|
5
|
Krzhizhanovskaya VV, Závodszky G, Lees MH, Dongarra JJ, Sloot PMA, Brissos S, Teixeira J. Link Prediction by Analyzing Temporal Behavior of Vertices. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7304046 DOI: 10.1007/978-3-030-50420-5_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Complexity and dynamics are challenging properties of real-world social networks. Link prediction in dynamic social networks is an essential problem in social network analysis. Although different methods have been proposed to enhance the performance of link prediction, these methods need significant improvement in accuracy. In this study, we focus on the temporal behavior of social networks to predict potential future interactions. We examine the evolving pattern of vertices of a given network \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\mathcal {G}$$\end{document} over time. We introduce a time-varying score function to evaluate the activeness of vertices that uses the number of new interactions and the number of frequent interactions with existing connections. To consider the impact of timestamps of the interactions, the score function engages a time difference of the current time and the time of the interaction occurred. Many existing studies ignored the weight of the link in the given network \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\mathcal {G}$$\end{document}, which brings the time-varied details of the links. We consider two additional objective functions in our model: a weighted shortest distance between any two nodes and a weighted common neighbor index. We used Multi-Layer Perceptron (MLP), a deep learning architecture as a classifier to predict the link formation in the future and define our model as a binary classification problem. To evaluate our model, we train and test with six real-world dynamic networks and compare it with state-of-the-art methods as well as classic methods. The results confirm that our proposed method outperforms most of the state-of-the-art methods.
Collapse
|
6
|
Aslan S, Kaya B. Time-aware link prediction based on strengthened projection in bipartite networks. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.08.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
7
|
Graph regularization weighted nonnegative matrix factorization for link prediction in weighted complex network. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.08.068] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
|
8
|
Gao X, Chen J, Huai N. Meta-Circuit machine: Inferencing human collaborative relationships in heterogeneous information networks. Inf Process Manag 2019. [DOI: 10.1016/j.ipm.2019.01.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
9
|
Chen X, Fang L, Yang T, Yang J, Bao Z, Wu D, Zhao J. The application of degree related clustering coefficient in estimating the link predictability and predicting missing links of networks. CHAOS (WOODBURY, N.Y.) 2019; 29:053135. [PMID: 31154789 DOI: 10.1063/1.5029866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 05/03/2019] [Indexed: 06/09/2023]
Abstract
Though a lot of valuable algorithms of link prediction have been created, it is still difficult to improve the accuracy of link prediction for some networks. Such difficulties may be due to the intrinsic topological features of these networks. To reveal the correlation between the network topology and the link predictability, we generate a group of artificial networks by keeping some structural features of an initial seed network. Based on these artificial networks and some real networks, we find that five topological measures including clustering coefficient, structural consistency, random walk entropy, network diameter, and average path length significantly show their impact on the link predictability. Then, we define a topological score that combines these important topological features. Specifically, it is an integration of structural consistency with degree-related clustering coefficient defined in this work. This topological score exhibits high correlation with the link predictability. Finally, we propose an algorithm for link prediction based on this topological score. Our experiment on eight real networks verifies good performance of this algorithm in link prediction, which supports the reasonability of the new topological score. This work could be insightful for the study of the link predictability.
Collapse
Affiliation(s)
- Xing Chen
- Fundamental Department, Army Logistic University of PLA, Chongqing 401311, China
| | - Ling Fang
- Fundamental Department, Army Logistic University of PLA, Chongqing 401311, China
| | - Tinghong Yang
- Fundamental Department, Army Logistic University of PLA, Chongqing 401311, China
| | - Jian Yang
- School of Pharmacy, Second Military Medical University, Shanghai 200433, China
| | - Zerong Bao
- Department of Military Logistic, Army Logistic University of PLA, Chongqing 401311, China
| | - Duzhi Wu
- Department of Economics, Rongzhi College of Chongqing Technology and Business University, Chongqing 401320, China
| | - Jing Zhao
- Institute of Interdisciplinary Complex Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201210, China
| |
Collapse
|
10
|
Zare H, Nikooie Pour MA, Moradi P. Enhanced recommender system using predictive network approach. PHYSICA A: STATISTICAL MECHANICS AND ITS APPLICATIONS 2019; 520:322-337. [DOI: 10.1016/j.physa.2019.01.053] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
|
11
|
Using a Time-Based Weighting Criterion to Enhance Link Prediction in Social Networks. ENTERP INF SYST-UK 2018. [DOI: 10.1007/978-3-319-93375-7_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
12
|
Wang W, Feng Y, Jiao P, Yu W. Kernel framework based on non-negative matrix factorization for networks reconstruction and link prediction. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.09.020] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
13
|
Guns R. Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases. JOURNAL OF DATA AND INFORMATION SCIENCE 2017. [DOI: 10.20309/jdis.201620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Purpose
This study aims to answer the question to what extent different types of networks can be used to predict future co-authorship among authors.
Design/methodology/approach
We compare three types of networks: unweighted networks, in which a link represents a past collaboration; weighted networks, in which links are weighted by the number of joint publications; and bipartite author-publication networks. The analysis investigates their relation to positive stability, as well as their potential in predicting links in future versions of the co-authorship network. Several hypotheses are tested.
Findings
Among other results, we find that weighted networks do not automatically lead to better predictions. Bipartite networks, however, outperform unweighted networks in almost all cases.
Research limitations
Only two relatively small case studies are considered.
Practical implications
The study suggests that future link prediction studies on co-occurrence networks should consider using the bipartite network as a training network.
Originality/value
This is the first systematic comparison of unweighted, weighted, and bipartite training networks in link prediction.
Collapse
Affiliation(s)
- Raf Guns
- Centre for R&D Monitoring (ECOOM) , University of Antwerp , Antwerp 2020 , Belgium
| |
Collapse
|
14
|
Zhou X, Ding L, Li Z, Wan R. Collaborator recommendation in heterogeneous bibliographic networks using random walks. INFORM RETRIEVAL J 2017. [DOI: 10.1007/s10791-017-9300-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
15
|
Shakibian H, Moghadam Charkari N. Mutual information model for link prediction in heterogeneous complex networks. Sci Rep 2017; 7:44981. [PMID: 28344326 PMCID: PMC5366872 DOI: 10.1038/srep44981] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 01/23/2017] [Indexed: 11/21/2022] Open
Abstract
Recently, a number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks. However, these indices suffer from two major drawbacks. Firstly, they are primarily dependent on the connectivity degrees of node pairs without considering the further information provided by the given meta-path. Secondly, most of them are required to use a single and usually symmetric meta-path in advance. Hence, employing a set of different meta-paths is not straightforward. To tackle with these problems, we propose a mutual information model for link prediction in heterogeneous complex networks. The proposed model, called as Meta-path based Mutual Information Index (MMI), introduces meta-path based link entropy to estimate the link likelihood and could be carried on a set of available meta-paths. This estimation measures the amount of information through the paths instead of measuring the amount of connectivity between the node pairs. The experimental results on a Bibliography network show that the MMI obtains high prediction accuracy compared with other popular similarity indices.
Collapse
Affiliation(s)
- Hadi Shakibian
- Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | | |
Collapse
|
16
|
Zhu B, Xia Y, Zhang XJ. Weight prediction in complex networks based on neighbor set. Sci Rep 2016; 6:38080. [PMID: 27905497 PMCID: PMC5131472 DOI: 10.1038/srep38080] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 11/03/2016] [Indexed: 11/26/2022] Open
Abstract
Link weights are essential to network functionality, so weight prediction is important for understanding weighted networks given incomplete real-world data. In this work, we develop a novel method for weight prediction based on the local network structure, namely, the set of neighbors of each node. The performance of this method is validated in two cases. In the first case, some links are missing altogether along with their weights, while in the second case all links are known and weight information is missing for some links. Empirical experiments on real-world networks indicate that our method can provide accurate predictions of link weights in both cases.
Collapse
Affiliation(s)
- Boyao Zhu
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
| | - Yongxiang Xia
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
| | - Xue-Jun Zhang
- School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
| |
Collapse
|