288 research outputs found
Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model
Networks arising from social, technological and natural domains exhibit rich
connectivity patterns and nodes in such networks are often labeled with
attributes or features. We address the question of modeling the structure of
networks where nodes have attribute information. We present a Multiplicative
Attribute Graph (MAG) model that considers nodes with categorical attributes
and models the probability of an edge as the product of individual attribute
link formation affinities. We develop a scalable variational expectation
maximization parameter estimation method. Experiments show that MAG model
reliably captures network connectivity as well as provides insights into how
different attributes shape the network structure.Comment: 15 pages, 7 figures, 7 table
Donor Retention in Online Crowdfunding Communities: A Case Study of DonorsChoose.org
Online crowdfunding platforms like DonorsChoose.org and Kickstarter allow
specific projects to get funded by targeted contributions from a large number
of people. Critical for the success of crowdfunding communities is recruitment
and continued engagement of donors. With donor attrition rates above 70%, a
significant challenge for online crowdfunding platforms as well as traditional
offline non-profit organizations is the problem of donor retention.
We present a large-scale study of millions of donors and donations on
DonorsChoose.org, a crowdfunding platform for education projects. Studying an
online crowdfunding platform allows for an unprecedented detailed view of how
people direct their donations. We explore various factors impacting donor
retention which allows us to identify different groups of donors and quantify
their propensity to return for subsequent donations. We find that donors are
more likely to return if they had a positive interaction with the receiver of
the donation. We also show that this includes appropriate and timely
recognition of their support as well as detailed communication of their impact.
Finally, we discuss how our findings could inform steps to improve donor
retention in crowdfunding communities and non-profit organizations.Comment: preprint version of WWW 2015 pape
From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews
Recommending products to consumers means not only understanding their tastes,
but also understanding their level of experience. For example, it would be a
mistake to recommend the iconic film Seven Samurai simply because a user enjoys
other action movies; rather, we might conclude that they will eventually enjoy
it -- once they are ready. The same is true for beers, wines, gourmet foods --
or any products where users have acquired tastes: the `best' products may not
be the most `accessible'. Thus our goal in this paper is to recommend products
that a user will enjoy now, while acknowledging that their tastes may have
changed over time, and may change again in the future. We model how tastes
change due to the very act of consuming more products -- in other words, as
users become more experienced. We develop a latent factor recommendation system
that explicitly accounts for each user's level of experience. We find that such
a model not only leads to better recommendations, but also allows us to study
the role of user experience and expertise on a novel dataset of fifteen million
beer, wine, food, and movie reviews.Comment: 11 pages, 7 figure
Multiplicative Attribute Graph Model of Real-World Networks
Large scale real-world network data such as social and information networks
are ubiquitous. The study of such social and information networks seeks to find
patterns and explain their emergence through tractable models. In most
networks, and especially in social networks, nodes have a rich set of
attributes (e.g., age, gender) associated with them.
Here we present a model that we refer to as the Multiplicative Attribute
Graphs (MAG), which naturally captures the interactions between the network
structure and the node attributes. We consider a model where each node has a
vector of categorical latent attributes associated with it. The probability of
an edge between a pair of nodes then depends on the product of individual
attribute-attribute affinities. The model yields itself to mathematical
analysis and we derive thresholds for the connectivity and the emergence of the
giant connected component, and show that the model gives rise to networks with
a constant diameter. We analyze the degree distribution to show that MAG model
can produce networks with either log-normal or power-law degree distributions
depending on certain conditions.Comment: 33 pages, 6 figure
Defining and Evaluating Network Communities based on Ground-truth
Nodes in real-world networks organize into densely linked communities where
edges appear with high concentration among the members of the community.
Identifying such communities of nodes has proven to be a challenging task
mainly due to a plethora of definitions of a community, intractability of
algorithms, issues with evaluation and the lack of a reliable gold-standard
ground-truth.
In this paper we study a set of 230 large real-world social, collaboration
and information networks where nodes explicitly state their group memberships.
For example, in social networks nodes explicitly join various interest based
social groups. We use such groups to define a reliable and robust notion of
ground-truth communities. We then propose a methodology which allows us to
compare and quantitatively evaluate how different structural definitions of
network communities correspond to ground-truth communities. We choose 13
commonly used structural definitions of network communities and examine their
sensitivity, robustness and performance in identifying the ground-truth. We
show that the 13 structural definitions are heavily correlated and naturally
group into four classes. We find that two of these definitions, Conductance and
Triad-participation-ratio, consistently give the best performance in
identifying ground-truth communities. We also investigate a task of detecting
communities given a single seed node. We extend the local spectral clustering
algorithm into a heuristic parameter-free community detection method that
easily scales to networks with more than hundred million nodes. The proposed
method achieves 30% relative improvement over current local clustering methods.Comment: Proceedings of 2012 IEEE International Conference on Data Mining
(ICDM), 201
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