20,788 research outputs found
Extraction and Analysis of Facebook Friendship Relations
Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud
However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms
Large scale homophily analysis in twitter using a twixonomy
In this paper we perform a large-scale homophily analysis on Twitter using a hierarchical representation of users' interests which we call a Twixonomy. In order to build a population, community, or single-user Twixonomy we first associate "topical" friends in users' friendship lists (i.e. friends representing an interest rather than a social relation between peers) with Wikipedia categories. A wordsense disambiguation algorithm is used to select the appropriate wikipage for each topical friend. Starting from the set of wikipages representing "primitive" interests, we extract all paths connecting these pages with topmost Wikipedia category nodes, and we then prune the resulting graph G efficiently so as to induce a direct acyclic graph. This graph is the Twixonomy. Then, to analyze homophily, we compare different methods to detect communities in a peer friends Twitter network, and then for each community we compute the degree of homophily on the basis of a measure of pairwise semantic similarity. We show that the Twixonomy provides a means for describing users' interests in a compact and readable way and allows for a fine-grained homophily analysis. Furthermore, we show that midlow level categories in the Twixonomy represent the best balance between informativeness and compactness of the representation
The anatomy of urban social networks and its implications in the searchability problem
The appearance of large geolocated communication datasets has recently
increased our understanding of how social networks relate to their physical
space. However, many recurrently reported properties, such as the spatial
clustering of network communities, have not yet been systematically tested at
different scales. In this work we analyze the social network structure of over
25 million phone users from three countries at three different scales: country,
provinces and cities. We consistently find that this last urban scenario
presents significant differences to common knowledge about social networks.
First, the emergence of a giant component in the network seems to be controlled
by whether or not the network spans over the entire urban border, almost
independently of the population or geographic extension of the city. Second,
urban communities are much less geographically clustered than expected. These
two findings shed new light on the widely-studied searchability in
self-organized networks. By exhaustive simulation of decentralized search
strategies we conclude that urban networks are searchable not through
geographical proximity as their country-wide counterparts, but through an
homophily-driven community structure
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