Accurate similarity index based on activity and connectivity of node for link prediction

2015 ◽  
Vol 29 (17) ◽  
pp. 1550108 ◽  
Author(s):  
Longjie Li ◽  
Lvjian Qian ◽  
Xiaoping Wang ◽  
Shishun Luo ◽  
Xiaoyun Chen
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Link Prediction ◽  
Similarity Index ◽  
Experimental Results ◽  
Network Data ◽  
Prediction Problem ◽  
Similarity Indices ◽  
Average Activity ◽  
Fundamental Requirement

Recent years have witnessed the increasing of available network data; however, much of those data is incomplete. Link prediction, which can find the missing links of a network, plays an important role in the research and analysis of complex networks. Based on the assumption that two unconnected nodes which are highly similar are very likely to have an interaction, most of the existing algorithms solve the link prediction problem by computing nodes' similarities. The fundamental requirement of those algorithms is accurate and effective similarity indices. In this paper, we propose a new similarity index, namely similarity based on activity and connectivity (SAC), which performs link prediction more accurately. To compute the similarity between two nodes, this index employs the average activity of these two nodes in their common neighborhood and the connectivities between them and their common neighbors. The higher the average activity is and the stronger the connectivities are, the more similar the two nodes are. The proposed index not only commendably distinguishes the contributions of paths but also incorporates the influence of endpoints. Therefore, it can achieve a better predicting result. To verify the performance of SAC, we conduct experiments on 10 real-world networks. Experimental results demonstrate that SAC outperforms the compared baselines.

scholarly journals Link Prediction through Deep Generative Model

2018 ◽  
Author(s):  
Xu-Wen Wang ◽  
Yize Chen ◽  
Yang-Yu Liu
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Link Prediction ◽  
Prediction Method ◽  
Generative Models ◽  
Generative Model ◽  
Superior Performance ◽  
Prediction Problem ◽  
Structural Patterns ◽  
Domain Specific

AbstractInferring missing links or predicting future ones based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine1–3, e-commerce4, social media5 and criminal intelligence6. Numerous methods have been proposed to solve the link prediction problem7–9. Yet, many of these existing methods are designed for undirected networks only. Moreover, most methods are based on domain-specific heuristics10, and hence their performances differ greatly for networks from different domains. Here we developed a new link prediction method based on deep generative models11 in machine learning. This method does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities12. Conceptually, taking into account structural patterns at different scales all together should outperform any domain-specific heuristics that typically focus on structural patterns at a particular scale. Indeed, when applied to various real-world networks from different domains13–17, our method shows overall superior performance against existing methods. Moreover, it can be easily parallelized by splitting a large network into several small subnetworks and then perform link prediction for each subnetwork in parallel. Our results imply that deep learning techniques can be effectively applied to complex networks and solve the classical link prediction problem with robust and superior performance.SummaryWe propose a new link prediction method based on deep generative models.

scholarly journals Visualizing real-world networks

2021 ◽  
Author(s):  
Lyndsay Roach
Keyword(s):  
Complex Networks ◽  
Real World ◽  
The Internet ◽  
Network Data ◽  
Community Structures ◽  
Large Networks ◽  
Computing Power ◽  
On Line ◽  
Using Data ◽  
The Impact

The study of networks has been propelled by improvements in computing power, enabling our ability to mine and store large amounts of network data. Moreover, the ubiquity of the internet has afforded us access to records of interactions that have previously been invisible. We are now able to study complex networks with anywhere from hundreds to billions of nodes; however, it is difficult to visualize large networks in a meaningful way. We explore the process of visualizing real-world networks. We first discuss the properties of complex networks and the mechanisms used in the network visualizing software Gephi. Then we provide examples of voting, trade, and linguistic networks using data extracted from on-line sources. We investigate the impact of hidden community structures on the analysis of these real-world networks.

Link prediction based on local weighted paths for complex networks

2017 ◽  
Vol 28 (04) ◽  
pp. 1750053
Author(s):  
Yabing Yao ◽  
Ruisheng Zhang ◽  
Fan Yang ◽  
Yongna Yuan ◽  
Rongjing Hu ◽  
...  
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Link Prediction ◽  
Structural Similarity ◽  
Prediction Performance ◽  
Topological Feature ◽  
Topological Features ◽  
Node Similarity ◽  
Weighted Paths ◽  
Path Dependent

As a significant problem in complex networks, link prediction aims to find the missing and future links between two unconnected nodes by estimating the existence likelihood of potential links. It plays an important role in understanding the evolution mechanism of networks and has broad applications in practice. In order to improve prediction performance, a variety of structural similarity-based methods that rely on different topological features have been put forward. As one topological feature, the path information between node pairs is utilized to calculate the node similarity. However, many path-dependent methods neglect the different contributions of paths for a pair of nodes. In this paper, a local weighted path (LWP) index is proposed to differentiate the contributions between paths. The LWP index considers the effect of the link degrees of intermediate links and the connectivity influence of intermediate nodes on paths to quantify the path weight in the prediction procedure. The experimental results on 12 real-world networks show that the LWP index outperforms other seven prediction baselines.

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.

scholarly journals Fast Parallel All-Subgraph Enumeration Using Multicore Machines

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Saeed Shahrivari ◽  
Saeed Jalili
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Main Memory ◽  
Important Task ◽  
Experimental Results ◽  
External Memory ◽  
Input Graph ◽  
Enumeration Algorithm ◽  
Parallel Solution ◽  
Processor Cores

Enumerating all subgraphs of an input graph is an important task for analyzing complex networks. Valuable information can be extracted about the characteristics of the input graph using all-subgraph enumeration. Notwithstanding, the number of subgraphs grows exponentially with growth of the input graph or by increasing the size of the subgraphs to be enumerated. Hence, all-subgraph enumeration is very time consuming when the size of the subgraphs or the input graph is big. We propose a parallel solution namedSubenumwhich in contrast to available solutions can perform much faster. Subenum enumerates subgraphs using edges instead of vertices, and this approach leads to a parallel and load-balanced enumeration algorithm that can have efficient execution on current multicore and multiprocessor machines. Also, Subenum uses a fast heuristic which can effectively accelerate non-isomorphism subgraph enumeration. Subenum can efficiently use external memory, and unlike other subgraph enumeration methods, it is not associated with the main memory limits of the used machine. Hence, Subenum can handle large input graphs and subgraph sizes that other solutions cannot handle. Several experiments are done using real-world input graphs. Compared to the available solutions, Subenum can enumerate subgraphs several orders of magnitude faster and the experimental results show that the performance of Subenum scales almost linearly by using additional processor cores.

scholarly journals Stacking models for nearly optimal link prediction in complex networks

2020 ◽  
Vol 117 (38) ◽  
pp. 23393-23400 ◽  
Author(s):  
Amir Ghasemian ◽  
Homa Hosseinmardi ◽  
Aram Galstyan ◽  
Edoardo M. Airoldi ◽  
Aaron Clauset
Keyword(s):  
Social Networks ◽  
Data Collection ◽  
Real World ◽  
Link Prediction ◽  
State Of The Art ◽  
Network Data ◽  
Prediction Errors ◽  
Partially Observed ◽  
Speed Up ◽  
Large Corpus

Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speed up network data collection and improve network model validation. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current methods are. We answer these questions by systematically evaluating 203 individual link predictor algorithms, representing three popular families of methods, applied to a large corpus of 550 structurally diverse networks from six scientific domains. We first show that individual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family is best, or worst, across all realistic inputs. We then exploit this diversity using network-based metalearning to construct a series of “stacked” models that combine predictors into a single algorithm. Applied to a broad range of synthetic networks, for which we may analytically calculate optimal performance, these stacked models achieve optimal or nearly optimal levels of accuracy. Applied to real-world networks, stacked models are superior, but their accuracy varies strongly by domain, suggesting that link prediction may be fundamentally easier in social networks than in biological or technological networks. These results indicate that the state of the art for link prediction comes from combining individual algorithms, which can achieve nearly optimal predictions. We close with a brief discussion of limitations and opportunities for further improvements.

scholarly journals Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Longjie Li ◽  
Shenshen Bai ◽  
Mingwei Leng ◽  
Lu Wang ◽  
Xiaoyun Chen
Keyword(s):  
Decision Making ◽  
Complex Networks ◽  
Similarity Measure ◽  
Real World ◽  
Link Prediction ◽  
State Of The Art ◽  
Multiple Attribute Decision Making ◽  
Ideal Solution ◽  
Multiple Attribute ◽  
Novel Method

Link prediction, which aims to forecast potential or missing links in a complex network based on currently observed information, has drawn growing attention from researchers. To date, a host of similarity-based methods have been put forward. Usually, one method harbors the idea that one similarity measure is applicable to various networks, and thus has performance fluctuation on different networks. In this paper, we propose a novel method to solve this issue by regarding link prediction as a multiple-attribute decision-making (MADM) problem. In the proposed method, we consider RA, LP, and CAR indices as the multiattribute for node pairs. The technique for order performance by similarity to ideal solution (TOPSIS) is adopted to aggregate the multiattribute and rank node pairs. The proposed method is not limited to only one similarity measure, but takes separate measures into account, since different networks may have different topological structures. Experimental results on 10 real-world networks manifest that the proposed method is superior in comparison to state-of-the-art methods.

scholarly journals Study on similarity indices for link prediction in opportunistic networks

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880319
Author(s):  
Xulin Cai ◽  
Jian Shu ◽  
Linlan Liu
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Opportunistic Networks ◽  
Network Evolution ◽  
Experimental Results ◽  
Superior Performance ◽  
Node Mobility ◽  
Opportunistic Network ◽  
Similarity Indices ◽  
Intermittent Contact

Link prediction aims to estimate the existence of links between nodes, using information of network structures and node properties. According to the characteristics of node mobility, node intermittent contact, and high delay of opportunistic network, novel similarity indices are constructed based on CN, AA, and RA. The indices CN, AA, and RA do not consider the historic information of networks. Similarity indices, T_CN, T_AA, and T_RA, based on temporal characteristics are proposed. These take the historic information of network evolution into consideration. Using historic information of the evolution of opportunistic networks and 2-hop neighbor information of the nodes, similarity indices based on the temporal-spatial characteristics, O_CN, O_AA, and O_RA, are proposed. Based on the imote traces cambridge (ITC) and detected social network (DSN) datasets, the experimental results indicate that similarity indices O_CN, O_AA, and O_RA outperform CN, AA, and RA. Furthermore, index O_AA has superior performance.

A spectral method to detect community structure based on distance modularity matrix

2017 ◽  
Vol 31 (20) ◽  
pp. 1750129 ◽  
Author(s):  
Jin-Xuan Yang ◽  
Xiao-Dong Zhang
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Spectral Method ◽  
Real World ◽  
Biological Networks ◽  
Community Organizations ◽  
Experimental Results ◽  
Time Consumption ◽  
Connected Vertex

There are many community organizations in social and biological networks. How to identify these community structure in complex networks has become a hot issue. In this paper, an algorithm to detect community structure of networks is proposed by using spectra of distance modularity matrix. The proposed algorithm focuses on the distance of vertices within communities, rather than the most weakly connected vertex pairs or number of edges between communities. The experimental results show that our method achieves better effectiveness to identify community structure for a variety of real-world networks and computer generated networks with a little more time-consumption.

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