1,084 research outputs found
Associative nature of event participation dynamics: a network theory approach
The affiliation with various social groups can be a critical factor when it
comes to quality of life of each individual, making such groups an essential
element of every society. The group dynamics, longevity and effectiveness
strongly depend on group's ability to attract new members and keep them engaged
in group activities. It was shown that high heterogeneity of scientist's
engagement in conference activities of the specific scientific community
depends on the balance between the numbers of previous attendances and
non-attendances and is directly related to scientist's association with that
community. Here we show that the same holds for leisure groups of the Meetup
website and further quantify individual members' association with the group. We
examine how structure of personal social networks is evolving with the event
attendance. Our results show that member's increasing engagement in the group
activities is primarily associated with the strengthening of already existing
ties and increase in the bonding social capital. We also show that Meetup
social networks mostly grow trough big events, while small events contribute to
the groups cohesiveness.Comment: 16 pages, 6 figs + Supporting information 7 pages, 8 fig
CC-PSM: A Preference-Aware Selection Model for Cloud Service Based on Consumer Community
In order to give full consideration to the consumer’s personal preference in cloud service selection strategies and improve the credibility of service prediction, a preference-aware cloud service selection model based on consumer community (CC-PSM) is presented in this work. The objective of CC-PSM is to select a service meeting a target consumer’s demands and preference. Firstly, the correlation between cloud consumers from a bipartite network for service selection is mined to compute the preference similarity between them. Secondly, an improved hierarchical clustering algorithm is designed to discover the consumer community with similar preferences so as to form the trusted groups for service recommendation. In the clustering process, a quantization function called community degree is given to evaluate the quality of community structure. Thirdly, a prediction model based on consumer community is built to predict a consumer’s evaluation on an unknown service. The experimental results show that CC-PSM can effectively partition the consumers based on their preferences and has good effectiveness in service selection applications
Bloggers Behavior and Emergent Communities in Blog Space
Interactions between users in cyberspace may lead to phenomena different from
those observed in common social networks. Here we analyse large data sets about
users and Blogs which they write and comment, mapped onto a bipartite graph. In
such enlarged Blog space we trace user activity over time, which results in
robust temporal patterns of user--Blog behavior and the emergence of
communities. With the spectral methods applied to the projection on weighted
user network we detect clusters of users related to their common interests and
habits. Our results suggest that different mechanisms may play the role in the
case of very popular Blogs. Our analysis makes a suitable basis for theoretical
modeling of the evolution of cyber communities and for practical study of the
data, in particular for an efficient search of interesting Blog clusters and
further retrieval of their contents by text analysis
FLEET: Butterfly Estimation from a Bipartite Graph Stream
We consider space-efficient single-pass estimation of the number of
butterflies, a fundamental bipartite graph motif, from a massive bipartite
graph stream where each edge represents a connection between entities in two
different partitions. We present a space lower bound for any streaming
algorithm that can estimate the number of butterflies accurately, as well as
FLEET, a suite of algorithms for accurately estimating the number of
butterflies in the graph stream. Estimates returned by the algorithms come with
provable guarantees on the approximation error, and experiments show good
tradeoffs between the space used and the accuracy of approximation. We also
present space-efficient algorithms for estimating the number of butterflies
within a sliding window of the most recent elements in the stream. While there
is a significant body of work on counting subgraphs such as triangles in a
unipartite graph stream, our work seems to be one of the few to tackle the case
of bipartite graph streams.Comment: This is the author's version of the work. It is posted here by
permission of ACM for your personal use. Not for redistribution. The
definitive version was published in Seyed-Vahid Sanei-Mehri, Yu Zhang, Ahmet
Erdem Sariyuce and Srikanta Tirthapura. "FLEET: Butterfly Estimation from a
Bipartite Graph Stream". The 28th ACM International Conference on Information
and Knowledge Managemen
Spectral Thresholds in the Bipartite Stochastic Block Model
We consider a bipartite stochastic block model on vertex sets and
, with planted partitions in each, and ask at what densities efficient
algorithms can recover the partition of the smaller vertex set.
When , multiple thresholds emerge. We first locate a sharp
threshold for detection of the partition, in the sense of the results of
\cite{mossel2012stochastic,mossel2013proof} and \cite{massoulie2014community}
for the stochastic block model. We then show that at a higher edge density, the
singular vectors of the rectangular biadjacency matrix exhibit a localization /
delocalization phase transition, giving recovery above the threshold and no
recovery below. Nevertheless, we propose a simple spectral algorithm, Diagonal
Deletion SVD, which recovers the partition at a nearly optimal edge density.
The bipartite stochastic block model studied here was used by
\cite{feldman2014algorithm} to give a unified algorithm for recovering planted
partitions and assignments in random hypergraphs and random -SAT formulae
respectively. Our results give the best known bounds for the clause density at
which solutions can be found efficiently in these models as well as showing a
barrier to further improvement via this reduction to the bipartite block model.Comment: updated version, will appear in COLT 201
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
With the availability of vast amounts of user visitation history on
location-based social networks (LBSN), the problem of Point-of-Interest (POI)
prediction has been extensively studied. However, much of the research has been
conducted solely on voluntary checkin datasets collected from social apps such
as Foursquare or Yelp. While these data contain rich information about
recreational activities (e.g., restaurants, nightlife, and entertainment),
information about more prosaic aspects of people's lives is sparse. This not
only limits our understanding of users' daily routines, but more importantly
the modeling assumptions developed based on characteristics of recreation-based
data may not be suitable for richer check-in data. In this work, we present an
analysis of education "check-in" data using WiFi access logs collected at
Purdue University. We propose a heterogeneous graph-based method to encode the
correlations between users, POIs, and activities, and then jointly learn
embeddings for the vertices. We evaluate our method compared to previous
state-of-the-art POI prediction methods, and show that the assumptions made by
previous methods significantly degrade performance on our data with dense(r)
activity signals. We also show how our learned embeddings could be used to
identify similar students (e.g., for friend suggestions).Comment: published in KDD'1
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