A New Link Prediction Algorithm Based on Local Links

Yang, Juan; Yang, Lei; Zhang, Pengye

doi:10.1007/978-3-319-23531-8_2

Cited by 11 publications

(10 citation statements)

References 17 publications

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“…In terms of topological similarity predictors, the library contains the implementations of the most known algorithms existing in the literature, including Common Neighbors, Adamic-Adard, Resource Allocation, Cannistraci Resource Allocation, and Jackard Index, among other predictors. ( Liben-Nowell & Kleinberg, 2007 ; Newman, 2001 ; Jaccard, 1901 ; Adamic & Adar, 2003 ; Ravasz et al, 2002 ; Papadimitriou, Symeonidis & Manolopoulos, 2012 ; Liu & Lü, 2010 ; Lichtenwalter, Lussier & Chawla, 2010 ; Yang, Yang & Zhang (2015) ; Yang, Lichtenwalter & Chawla, 2015 ; Zhu & Xia, 2015 ; Muscoloni & Cannistraci, 2017 ; Cannistraci, Alanis-Lobato & Ravasi, 2013 ; Daminelli et al, 2015 ). Due to their local nature, these algorithms can scale to very large networks, especially when executed on distributed architectures.…”

Section: Architecture and Functionalitiesmentioning

confidence: 99%

LinkPred: a high performance library for link prediction in complex networks

Kerrache

2021

PeerJ Computer Science

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The problem of determining the likelihood of the existence of a link between two nodes in a network is called link prediction. This is made possible thanks to the existence of a topological structure in most real-life networks. In other words, the topologies of networked systems such as the World Wide Web, the Internet, metabolic networks, and human society are far from random, which implies that partial observations of these networks can be used to infer information about undiscovered interactions. Significant research efforts have been invested into the development of link prediction algorithms, and some researchers have made the implementation of their methods available to the research community. These implementations, however, are often written in different languages and use different modalities of interaction with the user, which hinders their effective use. This paper introduces LinkPred, a high-performance parallel and distributed link prediction library that includes the implementation of the major link prediction algorithms available in the literature. The library can handle networks with up to millions of nodes and edges and offers a unified interface that facilitates the use and comparison of link prediction algorithms by researchers as well as practitioners.

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Section: Architecture and Functionalitiesmentioning

confidence: 99%

LinkPred: a high performance library for link prediction in complex networks

Kerrache

2021

PeerJ Computer Science

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“…Motivated by the cohesion between common neighbors and predicted nodes, both attributes, and topological features are examined in [57], as:…”

Section: Local Neighbors Link Index (Lnl)mentioning

confidence: 99%

Review on Learning and Extracting Graph Features for Link Prediction

Mutlu

Oghaz

Rajabi

et al. 2020

MAKE

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Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and, recently, knowledge graphs. Numerous studies utilized link prediction approaches in order sto find missing links or predict the likelihood of future links as well as employed for reconstruction networks, recommender systems, privacy control, etc. This work presents an extensive review of state-of-art methods and algorithms proposed on this subject and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning-based methods. Additionally, a collection of network data sets has been presented in this paper, which can be used in order to study link prediction. We conclude this study with a discussion of recent developments and future research directions.

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“…By comparing the results obtained using different topological similarity indexes on five co-authorship networks, they concluded that topological information indeed improves link prediction performance. This work has motivated the introduction of several topological similarity measures for link prediction [14][15][16][17] . Zhou et al introduced the resource allocation (RAL) 18 index that, similarly to ADA index, gives more weight to nodes with low degrees.…”

Section: Background and Related Workmentioning

confidence: 99%

A Scalable Similarity-Popularity Link Prediction Method

Kerrache

Alharbi

Benhidour

2020

Sci Rep

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Link prediction is the task of computing the likelihood that a link exists between two given nodes in a network. With countless applications in different areas of science and engineering, link prediction has received the attention of many researchers working in various disciplines. considerable research efforts have been invested into the development of increasingly accurate prediction methods. Most of the proposed algorithms, however, have limited use in practice because of their high computational requirements. The aim of this work is to develop a scalable link prediction algorithm that offers a higher overall predictive power than existing methods. the proposed solution falls into the class of global, parameter-free similarity-popularity-based methods, and in it, we assume that network topology is governed by three factors: popularity of the nodes, their similarity and the attraction induced by local neighbourhood. in our approach, popularity and neighbourhood-caused attraction are computed directly from the network topology and factored out by introducing a specific weight map, which is then used to estimate the dissimilarity between non-adjacent nodes through shortest path distances. We show through extensive experimental testing that the proposed method produces highly accurate predictions at a fraction of the computational cost required by existing global methods and at a low additional cost compared to local methods. the scalability of the proposed algorithm is demonstrated on several large networks having hundreds of thousands of nodes. The Internet, the World Wide Web, the brain and human society are some examples of systems that, despite seeming completely different at first sight, all share a fundamental property: they are all composed of interacting entities. Individual objects in these systems are not isolated, but rather connected through links or relationships. Mounting scientific evidence shows that these systems are better understood by investigating their properties as networks, where nodes represent individual components, and links refer to relationships, interactions or influences that exist among nodes 1. Network science aims at understanding and creating effective tools for characterizing and quantifying complex systems. The first step in this endeavour is to observe and record the existing interactions in order to build the network. In most cases, however, it is not possible to observe all interactions between the individual components. This can be due either to limitations in the data collection process or because certain relationships have not yet been established 2. The process of identifying the links that are missing from the network is known as link prediction. Recommending new friends or collaborators in social networks 3 , reconstructing networks 2 and discovering unknown interactions in biological networks 4 are few examples of the variety of applications that can benefit from predicting non-existing links. Link prediction has proven to be a challenging problem, and a lot of effor...

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A New Link Prediction Algorithm Based on Local Links

Cited by 11 publications

References 17 publications

LinkPred: a high performance library for link prediction in complex networks

LinkPred: a high performance library for link prediction in complex networks

Review on Learning and Extracting Graph Features for Link Prediction

A Scalable Similarity-Popularity Link Prediction Method

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