Temporal Link Prediction Using Time Series of Quasi-Local Node Similarity Measures

2016 ◽  
Author(s):  
Alper Ozcan ◽  
Sule Gunduz Oguducu
Keyword(s):  
Time Series ◽  
Link Prediction ◽  
Similarity Measures ◽  
Node Similarity

Multivariate Time Series Link Prediction for Evolving Heterogeneous Network

2019 ◽  
Vol 18 (01) ◽  
pp. 241-286 ◽  
Author(s):  
Alper Ozcan ◽  
Sule Gunduz Oguducu
Keyword(s):  
Time Series ◽  
Real World ◽  
Link Prediction ◽  
Multivariate Time Series ◽  
Similarity Measures ◽  
Dynamic Structure ◽  
Social Bookmarking ◽  
Node Connectivity ◽  
Worldwide Web ◽  
Heterogeneous Social Networks

Link prediction is considered as one of the key tasks in various data mining applications for recommendation systems, bioinformatics, security and worldwide web. The majority of previous works in link prediction mainly focus on the homogeneous networks which only consider one type of node and link. However, real-world networks have heterogeneous interactions and complicated dynamic structure, which make link prediction a more challenging task. In this paper, we have studied the problem of link prediction in the dynamic, undirected, weighted/unweighted, heterogeneous social networks which are composed of multiple types of nodes and links that change over time. We propose a novel method, called Multivariate Time Series Link Prediction for evolving heterogeneous networks that incorporate (1) temporal evolution of the network; (2) correlations between link evolution and multi-typed relationships; (3) local and global similarity measures; and (4) node connectivity information. Our proposed method and the previously proposed time series methods are evaluated experimentally on a real-world bibliographic network (DBLP) and a social bookmarking network (Delicious). Experimental results show that the proposed method outperforms the previous methods in terms of AUC measures in different test cases.

scholarly journals Proximity Measures as Graph Convolution Matrices for Link Prediction in Biological Networks

2020 ◽  
Author(s):  
Mustafa Coşkun ◽  
Mehmet Koyutürk
Keyword(s):  
Link Prediction ◽  
Similarity Measures ◽  
Graph Representation ◽  
Supplementary Information ◽  
Great Promise ◽  
Network Embedding ◽  
Common Neighbor ◽  
Node Similarity ◽  
Topological Characteristics ◽  
Low Dimensional

AbstractMotivationLink prediction is an important and well-studied problem in computational biology, with a broad range of applications including disease gene prioritization, drug-disease associations, and drug response in cancer. The general principle in link prediction is to use the topological characteristics and the attributes–if available– of the nodes in the network to predict new links that are likely to emerge/disappear. Recently, graph representation learning methods, which aim to learn a low-dimensional representation of topological characteristics and the attributes of the nodes, have drawn increasing attention to solve the link prediction problem via learnt low-dimensional features. Most prominently, Graph Convolution Network (GCN)-based network embedding methods have demonstrated great promise in link prediction due to their ability of capturing non-linear information of the network. To date, GCN-based network embedding algorithms utilize a Laplacian matrix in their convolution layers as the convolution matrix and the effect of the convolution matrix on algorithm performance has not been comprehensively characterized in the context of link prediction in biomedical networks. On the other hand, for a variety of biomedical link prediction tasks, traditional node similarity measures such as Common Neighbor, Ademic-Adar, and other have shown promising results, and hence there is a need to systematically evaluate the node similarity measures as convolution matrices in terms of their usability and potential to further the state-of-the-art.ResultsWe select 8 representative node similarity measures as convolution matrices within the single-layered GCN graph embedding method and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug–drug interaction (DDI) prediction, protein–protein interaction (PPI) prediction. Our experimental results demonstrate that the node similarity-based convolution matrices significantly improves GCN-based embedding algorithms and deserve more attention in the future biomedical link predictionAvailabilityOur method is implemented as a python library and is available at [email protected] informationSupplementary data are available at Bioinformatics online.

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.

Link prediction using time series of neighborhood-based node similarity scores

2015 ◽  
Vol 30 (1) ◽  
pp. 147-180 ◽  
Author(s):  
İsmail Güneş ◽  
Şule Gündüz-Öğüdücü ◽  
Zehra Çataltepe
Keyword(s):  
Time Series ◽  
Link Prediction ◽  
Node Similarity ◽  
Similarity Scores

An effective similarity measure based on kernel spectral method for complex networks

2019 ◽  
Vol 30 (07) ◽  
pp. 1940005
Author(s):  
Longjie Li ◽  
Lu Wang ◽  
Shenshen Bai ◽  
Shiyu Fang ◽  
Jianjun Cheng ◽  
...  
Keyword(s):  
Network Analysis ◽  
Spectral Method ◽  
Similarity Measure ◽  
Link Prediction ◽  
Structural Information ◽  
Critical Role ◽  
Similarity Measures ◽  
Global Information ◽  
Kernel Matrix ◽  
Node Similarity

Node similarity measure is a special important task in complex network analysis and plays a critical role in a multitude of applications, such as link prediction, community detection, and recommender systems. In this study, we are interested in link-based similarity measures, which only concern the structural information of networks when estimating node similarity. A new algorithm is proposed by adopting the idea of kernel spectral method to quantify the similarity of nodes. When computing the kernel matrix, the proposed algorithm makes use of local structural information, but it takes advantage of global information when constructing the feature matrix. Thence, the proposed algorithm could better capture potential relationships between nodes. To show the superiority of our algorithm over others, we conduct experiments on 10 real-world networks. Experimental results demonstrate that our algorithm yields more reasonable results and better performance of accuracy than baselines.

Link prediction based on heterogeneous degree penalization with extending neighbors and clustering coefficient

2021 ◽  
Author(s):  
Rongrong Song ◽  
Guang Ling ◽  
Qingju Fan ◽  
Ming-Feng Ge ◽  
Fang Wang
Keyword(s):  
Local Structure ◽  
Link Prediction ◽  
Similarity Index ◽  
Similarity Measures ◽  
Clustering Coefficient ◽  
The Other ◽  
Challenging Problem ◽  
Structure Information ◽  
Node Similarity ◽  
A Current

Link prediction, aiming to find missing links in a current network or to predict some possible new links in a future network, is a challenging problem in complex networks. Many existing link prediction algorithms perform the task by optimizing the node similarity measures, and then determining the possibility of the link between any pair of similar nodes. In this paper, we propose a novel node similarity index named heterogeneous degree penalization (HDP), which incorporates the quasi-local structure information of extending neighborhood of each pair of nodes to be predicted and the clustering coefficient of their common neighbors. For specific networks with different statistical properties, we can achieve a good performance of link prediction through adjusting the penalty weights. The experiment results show that, comparing with the other existing approaches, the proposed method can remarkably improve the accuracy of link prediction.

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.

scholarly journals On-line Elastic Similarity Measures for time series

2019 ◽  
Vol 88 ◽  
pp. 506-517 ◽  
Author(s):  
Izaskun Oregi ◽  
Aritz Pérez ◽  
Javier Del Ser ◽  
Jose A. Lozano
Keyword(s):  
Time Series ◽  
Similarity Measures ◽  
On Line
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