دانلود رایگان مقاله کشف اثر انگشت شبکه اجتماعی آنلاین با روش شبکه مبتنی بر موتیف

abstract

Complex networks facilitate the understanding of natural and man-made processes and are classified based on the concepts they model: biological, technological, social or semantic. The relevant subgraphs in these networks, called network motifs, are demonstrated to show core aspects of network functionality and can be used to analyze complex networks based on their topological fingerprint. We propose a novel approach of classifying social networks based on their topological aspects using motifs. As such, we define the classifiers for regular, random, small-world and scale-free topologies, and then apply this classification on empirical networks. We then show how our study brings a new perspective on differentiating between online social networks like Facebook, Twitter and Google Plus based on the distribution of network motifs over the fundamental topology classes. Characteristic patterns of motifs are obtained for each of the analyzed online networks and are used to better explain the functional properties behind how people interact online and to define classifiers capable of mapping any online network to a set of topological-communicational properties.

6. Conclusions

In this paper, we have shown that studying complex networks from a topological perspective, though the insight offered by network motifs, is a new fundamental approach in understanding the emergence of social networks. Indeed, motifs highlight functional aspects of the driving forces behind online social network creation, ties formation, community emergence, and overall communication trends. Our comprehensive social networks analysis, based on graph metric and fidelity assessments, has found a predisposition for characteristics of regular networks (geo-proximity drives tie formation), followed closely by random network aspects (long range link formation), then, with diminishing predisposition, by small-world properties (tendency to cluster and close triads), and, with very low occurrence, characteristics of scale-free networks (hub formation). Finally, we have shown that each online social platform has quite distinct properties, which imply distinct motif fingerprints, and thus different communication mechanisms.