26,547 research outputs found
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Influence of Personal Preferences on Link Dynamics in Social Networks
We study a unique network dataset including periodic surveys and electronic
logs of dyadic contacts via smartphones. The participants were a sample of
freshmen entering university in the Fall 2011. Their opinions on a variety of
political and social issues and lists of activities on campus were regularly
recorded at the beginning and end of each semester for the first three years of
study. We identify a behavioral network defined by call and text data, and a
cognitive network based on friendship nominations in ego-network surveys. Both
networks are limited to study participants. Since a wide range of attributes on
each node were collected in self-reports, we refer to these networks as
attribute-rich networks. We study whether student preferences for certain
attributes of friends can predict formation and dissolution of edges in both
networks. We introduce a method for computing student preferences for different
attributes which we use to predict link formation and dissolution. We then rank
these attributes according to their importance for making predictions. We find
that personal preferences, in particular political views, and preferences for
common activities help predict link formation and dissolution in both the
behavioral and cognitive networks.Comment: 12 page
Predicting links in ego-networks using temporal information
Link prediction appears as a central problem of network science, as it calls
for unfolding the mechanisms that govern the micro-dynamics of the network. In
this work, we are interested in ego-networks, that is the mere information of
interactions of a node to its neighbors, in the context of social
relationships. As the structural information is very poor, we rely on another
source of information to predict links among egos' neighbors: the timing of
interactions. We define several features to capture different kinds of temporal
information and apply machine learning methods to combine these various
features and improve the quality of the prediction. We demonstrate the
efficiency of this temporal approach on a cellphone interaction dataset,
pointing out features which prove themselves to perform well in this context,
in particular the temporal profile of interactions and elapsed time between
contacts.Comment: submitted to EPJ Data Scienc
Predicting Multi-actor collaborations using Hypergraphs
Social networks are now ubiquitous and most of them contain interactions
involving multiple actors (groups) like author collaborations, teams or emails
in an organizations, etc. Hypergraphs are natural structures to effectively
capture multi-actor interactions which conventional dyadic graphs fail to
capture. In this work the problem of predicting collaborations is addressed
while modeling the collaboration network as a hypergraph network. The problem
of predicting future multi-actor collaboration is mapped to hyperedge
prediction problem. Given that the higher order edge prediction is an
inherently hard problem, in this work we restrict to the task of predicting
edges (collaborations) that have already been observed in past. In this work,
we propose a novel use of hyperincidence temporal tensors to capture time
varying hypergraphs and provides a tensor decomposition based prediction
algorithm. We quantitatively compare the performance of the hypergraphs based
approach with the conventional dyadic graph based approach. Our hypothesis that
hypergraphs preserve the information that simple graphs destroy is corroborated
by experiments using author collaboration network from the DBLP dataset. Our
results demonstrate the strength of hypergraph based approach to predict higher
order collaborations (size>4) which is very difficult using dyadic graph based
approach. Moreover, while predicting collaborations of size>2 hypergraphs in
most cases provide better results with an average increase of approx. 45% in
F-Score for different sizes = {3,4,5,6,7}
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