6,228 research outputs found
The Directed Closure Process in Hybrid Social-Information Networks, with an Analysis of Link Formation on Twitter
It has often been taken as a working assumption that directed links in
information networks are frequently formed by "short-cutting" a two-step path
between the source and the destination -- a kind of implicit "link copying"
analogous to the process of triadic closure in social networks. Despite the
role of this assumption in theoretical models such as preferential attachment,
it has received very little direct empirical investigation. Here we develop a
formalization and methodology for studying this type of directed closure
process, and we provide evidence for its important role in the formation of
links on Twitter. We then analyze a sequence of models designed to capture the
structural phenomena related to directed closure that we observe in the Twitter
data
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
From sparse to dense and from assortative to disassortative in online social networks
Inspired by the analysis of several empirical online social networks, we
propose a simple reaction-diffusion-like coevolving model, in which individuals
are activated to create links based on their states, influenced by local
dynamics and their own intention. It is shown that the model can reproduce the
remarkable properties observed in empirical online social networks; in
particular, the assortative coefficients are neutral or negative, and the power
law exponents are smaller than 2. Moreover, we demonstrate that, under
appropriate conditions, the model network naturally makes transition(s) from
assortative to disassortative, and from sparse to dense in their
characteristics. The model is useful in understanding the formation and
evolution of online social networks.Comment: 10 pages, 7 figures and 2 table
Local rewiring rules for evolving complex networks
ERC is grateful for the nancial support of the EPSRC
Two types of well followed users in the followership networks of Twitter
In the Twitter blogosphere, the number of followers is probably the most
basic and succinct quantity for measuring popularity of users. However, the
number of followers can be manipulated in various ways; we can even buy
follows. Therefore, alternative popularity measures for Twitter users on the
basis of, for example, users' tweets and retweets, have been developed. In the
present work, we take a purely network approach to this fundamental question.
First, we find that two relatively distinct types of users possessing a large
number of followers exist, in particular for Japanese, Russian, and Korean
users among the seven language groups that we examined. A first type of user
follows a small number of other users. A second type of user follows
approximately the same number of other users as the number of follows that the
user receives. Then, we compare local (i.e., egocentric) followership networks
around the two types of users with many followers. We show that the second
type, which is presumably uninfluential users despite its large number of
followers, is characterized by high link reciprocity, a large number of friends
(i.e., those whom a user follows) for the followers, followers' high link
reciprocity, large clustering coefficient, large fraction of the second type of
users among the followers, and a small PageRank. Our network-based results
support that the number of followers used alone is a misleading measure of
user's popularity. We propose that the number of friends, which is simple to
measure, also helps us to assess the popularity of Twitter users.Comment: 4 Figures and 8 Table
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