Generating online social networks based on socio-demographic attributes

Abstract

Recent years have seen tremendous growth of many online social networks such as Facebook, LinkedIn and MySpace. People connect to each other through these networks forming large social communities providing researchers rich datasets to understand, model and predict social interactions and behaviors. New contacts in these networks can be formed due to an individual's demographic attributes such as age group, gender, geographic location, or due to a network's structural dynamics such as triadic closure and preferential attachment, or a combination of both demographic and structural characteristics.A number of network generation models have been proposed in the last decade to explain the structure, evolution and processes taking place in different types of networks, and notably social networks. Network generation models studied in the literature primarily consider structural properties, and in some cases an individual's demographic profile in the formation of new social contacts. These models do not present a mechanism to combine both structural and demographic characteristics for the formation of new links. In this paper, we propose a new network generation algorithm which incorporates both these characteristics to model network formation. We use different publicly available Facebook datasets as benchmarks to demonstrate the correctness of the proposed network generation model. The proposed model is flexible and thus can generate networks with varying demographic and structural properties.

Analysis of Facebook Interaction as Basis for Synthetic Expanded Social Graph Generation

“…If the social graph was generated only based on a known node-number distribution, we would get a fairly homogeneous network without some characteristic features of empirical social networks. Many studies have focused specifically towards the creation of a model for generating social graphs [11]- [15], [24], [30], [31]. The shared goal between them is to generate a synthetic social graph that has characteristics similar to an empirical social graph, but using different approaches.…”

“…the number of nodes. Then, using some of the existing approaches [11]- [15], [24], [30], [31], [57] we can generate the binary friendship graph of the selected size. Siddula et al [29] suggest the Small World Topology algorithm [12] as a very good representation of OSN binary social graph, thus we recommend using this algorithm for generating a binary friendship graph.…”

“…Throughout the years, various generators of synthetic social graphs [11]- [15], [24], [30], [31] have been implemented. One thing these all generators have in common is that they exclusively produce binary social graphs, i.e.…”

Discriminative Distance-Based Network Indices with Application to Link Prediction

“…As a special case, for a fixed k, consider the list L(k) consisting of all pairs of unconnected vertices u and such that d(u, ) = k. A network may have many such pairs. It is known that compared to the pairs of unconnected vertices that have distance k + 1, members of L(k) are more likely to form a link during the test interval [17,58]. However, the question remaining open is what elements of L(k) are more likely to be connected than the other members?…”

Rethinking the Concept of Just Noticeable Difference in Online Marketing

“…Holme & Kim (2002) proposed a way how to complement preferential attachment models with triad formation which generates networks with both scale-free and small world properties. Pasta, Zaidi, & Rozenblat (2014) added principles how to involve demographic properties to such networks. And Li et al (2014) showed how it is possible to artificially build social networks which are sparse/dense and assortative/dissassortative and where these properties my change over the social network evolution.…”