10,227 research outputs found
Finding Neighbor Communities in the Web Using an Inter-Site Graph
In this paper, we present Neighbor Community Finder (NCF, for short), a tool for finding Web communities related to given URLs. While existing link-based methods of finding communities, such as HITS, trawling, and Companion, use algorithms running on a Web graph whose vertices are pages and edges are links on the Web, NCF uses an algorithm running on an inter-site graph whose vertices are sites and edges are global-links (links between sites). Since the phrase "Web site" is used ambiguously in our daily life and has no unique definition, NCF uses directory-based sites proposed by the authors as a model of Web sites. NCF receives URLs interested in by a user and constructs an inter-site graph containing neighbor sites of the given URLs by using a method of identifying directory-based sites from URL and link data obtained from the actual Web on demand. By computational experiments, we show that NCF achieves higher quality than Google\u27s "Similar Pages" service for finding pages related to given URLs corresponding to various topics selected from among the directories of Yahoo! Japan.PAPE
The Impact of Network Flows on Community Formation in Models of Opinion Dynamics
We study dynamics of opinion formation in a network of coupled agents. As the
network evolves to a steady state, opinions of agents within the same community
converge faster than those of other agents. This framework allows us to study
how network topology and network flow, which mediates the transfer of opinions
between agents, both affect the formation of communities. In traditional models
of opinion dynamics, agents are coupled via conservative flows, which result in
one-to-one opinion transfer. However, social interactions are often
non-conservative, resulting in one-to-many transfer of opinions. We study
opinion formation in networks using one-to-one and one-to-many interactions and
show that they lead to different community structure within the same network.Comment: accepted for publication in The Journal of Mathematical Sociology.
arXiv admin note: text overlap with arXiv:1201.238
Potential Networks, Contagious Communities, and Understanding Social Network Structure
In this paper we study how the network of agents adopting a particular
technology relates to the structure of the underlying network over which the
technology adoption spreads. We develop a model and show that the network of
agents adopting a particular technology may have characteristics that differ
significantly from the social network of agents over which the technology
spreads. For example, the network induced by a cascade may have a heavy-tailed
degree distribution even if the original network does not.
This provides evidence that online social networks created by technology
adoption over an underlying social network may look fundamentally different
from social networks and indicates that using data from many online social
networks may mislead us if we try to use it to directly infer the structure of
social networks. Our results provide an alternate explanation for certain
properties repeatedly observed in data sets, for example: heavy-tailed degree
distribution, network densification, shrinking diameter, and network community
profile. These properties could be caused by a sort of `sampling bias' rather
than by attributes of the underlying social structure. By generating networks
using cascades over traditional network models that do not themselves contain
these properties, we can nevertheless reliably produce networks that contain
all these properties.
An opportunity for interesting future research is developing new methods that
correctly infer underlying network structure from data about a network that is
generated via a cascade spread over the underlying network.Comment: To Appear in Proceedings of the 22nd International World Wide Web
Conference(WWW 2013
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
Evolution of Ego-networks in Social Media with Link Recommendations
Ego-networks are fundamental structures in social graphs, yet the process of
their evolution is still widely unexplored. In an online context, a key
question is how link recommender systems may skew the growth of these networks,
possibly restraining diversity. To shed light on this matter, we analyze the
complete temporal evolution of 170M ego-networks extracted from Flickr and
Tumblr, comparing links that are created spontaneously with those that have
been algorithmically recommended. We find that the evolution of ego-networks is
bursty, community-driven, and characterized by subsequent phases of explosive
diameter increase, slight shrinking, and stabilization. Recommendations favor
popular and well-connected nodes, limiting the diameter expansion. With a
matching experiment aimed at detecting causal relationships from observational
data, we find that the bias introduced by the recommendations fosters global
diversity in the process of neighbor selection. Last, with two link prediction
experiments, we show how insights from our analysis can be used to improve the
effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl
The structure and function of complex networks
Inspired by empirical studies of networked systems such as the Internet,
social networks, and biological networks, researchers have in recent years
developed a variety of techniques and models to help us understand or predict
the behavior of these systems. Here we review developments in this field,
including such concepts as the small-world effect, degree distributions,
clustering, network correlations, random graph models, models of network growth
and preferential attachment, and dynamical processes taking place on networks.Comment: Review article, 58 pages, 16 figures, 3 tables, 429 references,
published in SIAM Review (2003
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