178,020 research outputs found
Studying Fake News via Network Analysis: Detection and Mitigation
Social media for news consumption is becoming increasingly popular due to its
easy access, fast dissemination, and low cost. However, social media also
enable the wide propagation of "fake news", i.e., news with intentionally false
information. Fake news on social media poses significant negative societal
effects, and also presents unique challenges. To tackle the challenges, many
existing works exploit various features, from a network perspective, to detect
and mitigate fake news. In essence, news dissemination ecosystem involves three
dimensions on social media, i.e., a content dimension, a social dimension, and
a temporal dimension. In this chapter, we will review network properties for
studying fake news, introduce popular network types and how these networks can
be used to detect and mitigation fake news on social media.Comment: Submitted as a invited book chapter in Lecture Notes in Social
Networks, Springer Pres
Overlapping Community Discovery Methods: A Survey
The detection of overlapping communities is a challenging problem which is
gaining increasing interest in recent years because of the natural attitude of
individuals, observed in real-world networks, to participate in multiple groups
at the same time. This review gives a description of the main proposals in the
field. Besides the methods designed for static networks, some new approaches
that deal with the detection of overlapping communities in networks that change
over time, are described. Methods are classified with respect to the underlying
principles guiding them to obtain a network division in groups sharing part of
their nodes. For each of them we also report, when available, computational
complexity and web site address from which it is possible to download the
software implementing the method.Comment: 20 pages, Book Chapter, appears as Social networks: Analysis and Case
Studies, A. Gunduz-Oguducu and A. S. Etaner-Uyar eds, Lecture Notes in Social
Networks, pp. 105-125, Springer,201
Fighting with the Sparsity of Synonymy Dictionaries
Graph-based synset induction methods, such as MaxMax and Watset, induce
synsets by performing a global clustering of a synonymy graph. However, such
methods are sensitive to the structure of the input synonymy graph: sparseness
of the input dictionary can substantially reduce the quality of the extracted
synsets. In this paper, we propose two different approaches designed to
alleviate the incompleteness of the input dictionaries. The first one performs
a pre-processing of the graph by adding missing edges, while the second one
performs a post-processing by merging similar synset clusters. We evaluate
these approaches on two datasets for the Russian language and discuss their
impact on the performance of synset induction methods. Finally, we perform an
extensive error analysis of each approach and discuss prominent alternative
methods for coping with the problem of the sparsity of the synonymy
dictionaries.Comment: In Proceedings of the 6th Conference on Analysis of Images, Social
Networks, and Texts (AIST'2017): Springer Lecture Notes in Computer Science
(LNCS
A genetic algorithm
Castelli, M., Dondi, R., Manzoni, S., Mauri, G., & Zoppis, I. (2019). Top k 2-clubs in a network: A genetic algorithm. In J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, R. Lam, V. V. Krzhizhanovskaya, M. H. Lees, ... P. M. A. Sloot (Eds.), Computational Science. ICCS 2019: 19th International Conference, 2019, Proceedings (Vol. 5, pp. 656-663). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11540 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22750-0_63The identification of cohesive communities (dense sub-graphs) is a typical task applied to the analysis of social and biological networks. Different definitions of communities have been adopted for particular occurrences. One of these, the 2-club (dense subgraphs with diameter value at most of length 2) has been revealed of interest for applications and theoretical studies. Unfortunately, the identification of 2-clubs is a computationally intractable problem, and the search of approximate solutions (at a reasonable time) is therefore fundamental in many practical areas. In this article, we present a genetic algorithm based heuristic to compute a collection of Top k 2-clubs, i.e., a set composed by the largest k 2-clubs which cover an input graph. In particular, we discuss some preliminary results for synthetic data obtained by sampling Erdös-Rényi random graphs.authorsversionpublishe
A new model for selfish routing
AbstractIn this work, we introduce and study a new, potentially rich model for selfish routing over non-cooperative networks, as an interesting hybridization of the two prevailing such models, namely the KPmodel [E. Koutsoupias, C.H. Papadimitriou, Worst-case equilibria, in: G. Meinel, S. Tison (Eds.), Proceedings of the 16th Annual Symposium on Theoretical Aspects of Computer Science, in: Lecture Notes in Computer Science, vol. 1563, Springer-Verlag, 1999, pp. 404–413] and the Wmodel [J.G. Wardrop, Some theoretical aspects of road traffic research, Proceedings of the of the Institute of Civil Engineers 1 (Pt. II) (1952) 325–378].In the hybrid model, each of n users is using a mixed strategy to ship its unsplittable traffic over a network consisting of m parallel links. In a Nash equilibrium, no user can unilaterally improve its Expected Individual Cost. To evaluate Nash equilibria, we introduce Quadratic Social Cost as the sum of the expectations of the latencies, incurred by the squares of the accumulated traffic. This modeling is unlike the KP model, where Social Cost [E. Koutsoupias, C.H. Papadimitriou, Worst-case equilibria, in: G. Meinel, S. Tison (Eds.), Proceedings of the 16th Annual Symposium on Theoretical Aspects of Computer Science, in: Lecture Notes in Computer Science, vol. 1563, Springer-Verlag, 1999, pp. 404–413] is the expectation of the maximum latency incurred by the accumulated traffic; but it is like the W model since the Quadratic Social Cost can be expressed as a weighted sum of Expected Individual Costs. We use the Quadratic Social Cost to define Quadratic Coordination Ratio. Here are our main findings: •Quadratic Social Cost can be computed in polynomial time. This is unlike the #P-completeness [D. Fotakis, S. Kontogiannis, E. Koutsoupias, M. Mavronicolas, P. Spirakis, The structure and complexity of Nash equilibria for a selfish routing game, in: P. Widmayer, F. Triguero, R. Morales, M. Hennessy, S. Eidenbenz, R. Conejo (Eds.), Proceedings of the 29th International Colloquium on Automata, Languages and Programming, in: Lecture Notes in Computer Science, vol. 2380, Springer-Verlag, 2002, pp. 123–134] of computing Social Cost for the KP model.•For the case of identical users and identical links, the fully mixed Nash equilibrium [M. Mavronicolas, P. Spirakis, The price of selfish routing, Algorithmica 48 (1) (2007) 91–126], where each user assigns positive probability to every link, maximizes Quadratic Social Cost.•As our main result, we present a comprehensive collection of tight, constant (that is, independent of m and n), strictly less than 2, lower and upper bounds on the Quadratic Coordination Ratio for several, interesting special cases. Some of the bounds stand in contrast to corresponding super-constant bounds on the Coordination Ratio previously shown in [A. Czumaj, B. Vöcking, Tight bounds for worst-case equilibria, ACM Transactions on Algorithms 3 (1) (2007); E. Koutsoupias, M. Mavronicolas, P. Spirakis, Approximate equilibria and ball fusion, Theory of Computing Systems 36 (6) (2003) 683–693; E. Koutsoupias, C.H. Papadimitriou, Worst-case equilibria, in: G. Meinel, S. Tison (Eds.), Proceedings of the 16th Annual Symposium on Theoretical Aspects of Computer Science, in: Lecture Notes in Computer Science, vol. 1563, Springer-Verlag, 1999, pp. 404–413; M. Mavronicolas, P. Spirakis, The price of selfish routing, Algorithmica 48 (1) (2007) 91–126] for the KP model
Networks and network analysis for defence and security - a book review
It is intended in this work to review the book "Networks and Network Analysis for Defence and Security", 978-3-319-04146-9 published in Springer Series “Lecture Notes in Social Networks”. In this book the following areas are covered: Defence and security risk analysis; Criminal intelligence; Cybercrime; Cognitive analysis; Counter-terrorism and Social Network Analysis; Transnational Crime; Critical infrastructure analysis; Support to defence and security intelligence, emphasizing the idea that network analysis is a “key enabler in supporting defence and security”. Not only man-made threats against nation’s security are considered but also the ones resulting from natural hazards. With the emergence and enormous progress in “big data” analysis together with innovative interpretative approaches, network analysis facilitates a greater understanding of complex networks: their entities, interdependencies and vulnerabilities.peer-reviewe
Do Social Bots Dream of Electric Sheep? A Categorisation of Social Media Bot Accounts
So-called 'social bots' have garnered a lot of attention lately. Previous
research showed that they attempted to influence political events such as the
Brexit referendum and the US presidential elections. It remains, however,
somewhat unclear what exactly can be understood by the term 'social bot'. This
paper addresses the need to better understand the intentions of bots on social
media and to develop a shared understanding of how 'social' bots differ from
other types of bots. We thus describe a systematic review of publications that
researched bot accounts on social media. Based on the results of this
literature review, we propose a scheme for categorising bot accounts on social
media sites. Our scheme groups bot accounts by two dimensions - Imitation of
human behaviour and Intent.Comment: Accepted for publication in the Proceedings of the Australasian
Conference on Information Systems, 201
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