Przemyslaw Kazienko

Reda Alhajj

Jaideep Srivastava

Computational Aspects of Social Network Analysis

Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish &#x201c;prediction friendly&#x201d; networks, for which most of the prediction methods give good performance, as well as &#x201c;prediction unfriendly&#x201d; networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.

Fei Gao

Katarzyna Musial

Colin Cooper

Sophia Tsoka

Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

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 named Subenum which 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.

Saeed Shahrivari

Saeed Jalili

Fast Parallel All-Subgraph Enumeration Using Multicore Machines

Communities in networks are groups of nodes whose connections to the nodes in a community are stronger than with the nodes in the rest of the network. Quite often nodes participate in multiple communities; that is, communities can overlap. In this paper, we first analyze what other researchers have done to utilize high performance computing to perform efficient community detection in social, biological, and other networks. We note that detection of overlapping communities is more computationally intensive than disjoint community detection, and the former presents new challenges that algorithm designers have to face. Moreover, the efficiency of many existing algorithms grows superlinearly with the network size making them unsuitable to process large datasets. We use the Speaker-Listener Label Propagation Algorithm (SLPA) as the basis for our parallel overlapping community detection implementation. SLPA provides near linear time overlapping community detection and is well suited for parallelization. We explore the benefits of a multithreaded programming paradigm and show that it yields a significant performance gain over sequential execution while preserving the high quality of community detection. The algorithm was tested on four real-world datasets with up to 5.5 million nodes and 170 million edges. In order to assess the quality of community detection, at least 4 different metrics were used for each of the datasets.

Konstantin Kuzmin

Mingming Chen

Boleslaw K. Szymanski

Parallelizing SLPA for Scalable Overlapping Community Detection

Link recommendation is a popular research subject in the field of social network analysis and mining. Often, the main emphasis is put on the development of new recommendation algorithms, semantic enhancements to existing solutions, design of new similarity measures, and so forth. However, relatively little scientific attention has been paid to the impact that various data representation models have on the performance of recommendation algorithms. And by performance we do not mean the time or memory efficiency of algorithms, but the precision and recall of recommender systems. Our recent findings unanimously show that the choice of network representation model has an important and measurable impact on the quality of recommendations. In this paper we argue that the computation quality of link recommendation algorithms depends significantly on the social network representation and we advocate the use of actor-fact matrix as the best alternative. We verify our findings using several state-of-the-art link recommendation algorithms, such as SVD, RSVD, and RRI using both single-relation and multirelation dataset.

Micha&#x142; Ciesielczyk

Andrzej Szwabe

Miko&#x142;aj Morzy

On Efficient Link Recommendation in Social Networks Using Actor-Fact Matrices

The present paper introduces a hybrid technique to measure the expertise of users by analyzing their profiles and activities in social networks. Currently, both job seekers and talent hunters are looking for new and innovative techniques to filter jobs and candidates where candidates are trying to improve and make their profiles more attractive. In this sense, the Skillrank approach is based on the conjunction of existing and well-known information and expertise retrieval techniques that perfectly fit the existing web and social media environment to deliver an intelligent component to integrate the user context in the analysis of skills confidence. A major outcome of this approach is that it actually takes advantage of existing data and information available on the web to perform both a ranked list of experts in a field and a confidence value for every professional skill. Thus, expertise and experts can be detected, verified, and ranked using a suited trust metric. An experiment to validate the Skillrank technique based on precision and recall metrics is also presented using two different datasets: (1) ad hoc created using real data from a professional social network and (2) real data extracted from the LinkedIn API.

Jose Mar&#xed;a &#xc1;lvarez-Rodr&#xed;guez

Ricardo Colomo-Palacios

Vladimir Stantchev

Skillrank: Towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social Networks

In signed social networks, relationships among nodes are of the types positive (friendship) and negative (hostility). One absorbing issue in signed social networks is predicting sign of edges among people who are members of these networks. Other than edge sign prediction, one can define importance of people or nodes in networks via ranking algorithms.  There exist few ranking algorithms for signed graphs; also few studies have shown role of ranking in link prediction problem. Hence, we were motivated to investigate ranking algorithms availed for signed graphs and their effect on sign prediction problem. This paper makes the contribution of using community detection approach for ranking algorithms in signed graphs. Therefore, community detection which is another active area of research in social networks is also investigated in this paper. Community detection algorithms try to find groups of nodes in which they share common properties like similarity. We were able to devise three community-based ranking algorithms which are suitable for signed graphs, and also we evaluated these ranking algorithms via sign prediction problem. These ranking algorithms were tested on three large-scale datasets: Epinions, Slashdot, and Wikipedia. We indicated that, in some cases, these ranking algorithms outperform previous works because their prediction accuracies are better.

Saeed Reza Shahriary

Mohsen Shahriari

Rafidah MD Noor

A Community-Based Approach for Link Prediction in Signed Social Networks

Blogs are popular way to express opinions on the Internet. Due to their popularity and their public character blogs attract attention of many researchers. In this paper we compare two national blogospheres (Polish and American) from different angles such as characteristics of messages and interactions, structure of social groups, topics discussed in them, and the influence of real-world events on the behavior of such groups. In our approach we try to combine in advanced manner users activity on both the individual and community level. The comparison reveals some differences and various characters of both portals. Methods for analysis of groups dynamics, users roles, and topics in groups are presented.

Anna Zygmunt

Bogdan Gliwa

The Comparison of Users Activity on the Example of Polish and American Blogosphere

About this journal

Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

Journal profile

Meet the editorial board

Chief Editor&nbsp;Professor Tramontana&nbsp;is based at the University of Catania and his research primarily concerns the areas of software engineering and distributed systems.

Editor spotlight

Submit your proposal

Do you think there is an emerging area of research that really needs to be highlighted? Or an existing research area that has been overlooked or would benefit from deeper investigation? Raise the profile of a research area by leading a Special Issue.

Special Issues

Author guidelines

Editorial board

Databases and indexing

title

Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

description

https://images.hindawi.com/journals/sp/sp.banner.jpg

twitter:card

twitter:description

twitter:site

twitter:title

twitter:url

twitter:image

Scientific Programming | Hindawi

Scientific Programming

This Issue is now open for submissions.
Papers are published upon acceptance, regardless of the Special Issue publication date.

Scientific Programming

Computational Aspects of Social Network Analysis

Computational Aspects of Social Network Analysis

Articles