12,029 research outputs found
Latent Self-Exciting Point Process Model for Spatial-Temporal Networks
We propose a latent self-exciting point process model that describes
geographically distributed interactions between pairs of entities. In contrast
to most existing approaches that assume fully observable interactions, here we
consider a scenario where certain interaction events lack information about
participants. Instead, this information needs to be inferred from the available
observations. We develop an efficient approximate algorithm based on
variational expectation-maximization to infer unknown participants in an event
given the location and the time of the event. We validate the model on
synthetic as well as real-world data, and obtain very promising results on the
identity-inference task. We also use our model to predict the timing and
participants of future events, and demonstrate that it compares favorably with
baseline approaches.Comment: 20 pages, 6 figures (v3); 11 pages, 6 figures (v2); previous version
appeared in the 9th Bayesian Modeling Applications Workshop, UAI'1
Network Model Selection for Task-Focused Attributed Network Inference
Networks are models representing relationships between entities. Often these
relationships are explicitly given, or we must learn a representation which
generalizes and predicts observed behavior in underlying individual data (e.g.
attributes or labels). Whether given or inferred, choosing the best
representation affects subsequent tasks and questions on the network. This work
focuses on model selection to evaluate network representations from data,
focusing on fundamental predictive tasks on networks. We present a modular
methodology using general, interpretable network models, task neighborhood
functions found across domains, and several criteria for robust model
selection. We demonstrate our methodology on three online user activity
datasets and show that network model selection for the appropriate network task
vs. an alternate task increases performance by an order of magnitude in our
experiments
Cultural Diffusion and Trends in Facebook Photographs
Online social media is a social vehicle in which people share various moments
of their lives with their friends, such as playing sports, cooking dinner or
just taking a selfie for fun, via visual means, that is, photographs. Our study
takes a closer look at the popular visual concepts illustrating various
cultural lifestyles from aggregated, de-identified photographs. We perform
analysis both at macroscopic and microscopic levels, to gain novel insights
about global and local visual trends as well as the dynamics of interpersonal
cultural exchange and diffusion among Facebook friends. We processed images by
automatically classifying the visual content by a convolutional neural network
(CNN). Through various statistical tests, we find that socially tied
individuals more likely post images showing similar cultural lifestyles. To
further identify the main cause of the observed social correlation, we use the
Shuffle test and the Preference-based Matched Estimation (PME) test to
distinguish the effects of influence and homophily. The results indicate that
the visual content of each user's photographs are temporally, although not
necessarily causally, correlated with the photographs of their friends, which
may suggest the effect of influence. Our paper demonstrates that Facebook
photographs exhibit diverse cultural lifestyles and preferences and that the
social interaction mediated through the visual channel in social media can be
an effective mechanism for cultural diffusion.Comment: 10 pages, To appear in ICWSM 2017 (Full Paper
Inferring Population Preferences via Mixtures of Spatial Voting Models
Understanding political phenomena requires measuring the political
preferences of society. We introduce a model based on mixtures of spatial
voting models that infers the underlying distribution of political preferences
of voters with only voting records of the population and political positions of
candidates in an election. Beyond offering a cost-effective alternative to
surveys, this method projects the political preferences of voters and
candidates into a shared latent preference space. This projection allows us to
directly compare the preferences of the two groups, which is desirable for
political science but difficult with traditional survey methods. After
validating the aggregated-level inferences of this model against results of
related work and on simple prediction tasks, we apply the model to better
understand the phenomenon of political polarization in the Texas, New York, and
Ohio electorates. Taken at face value, inferences drawn from our model indicate
that the electorates in these states may be less bimodal than the distribution
of candidates, but that the electorates are comparatively more extreme in their
variance. We conclude with a discussion of limitations of our method and
potential future directions for research.Comment: To be published in the 8th International Conference on Social
Informatics (SocInfo) 201
Chinese Internet AS-level Topology
We present the first complete measurement of the Chinese Internet topology at
the autonomous systems (AS) level based on traceroute data probed from servers
of major ISPs in mainland China. We show that both the Chinese Internet AS
graph and the global Internet AS graph can be accurately reproduced by the
Positive-Feedback Preference (PFP) model with the same parameters. This result
suggests that the Chinese Internet preserves well the topological
characteristics of the global Internet. This is the first demonstration of the
Internet's topological fractality, or self-similarity, performed at the level
of topology evolution modeling.Comment: This paper is a preprint of a paper submitted to IEE Proceedings on
Communications and is subject to Institution of Engineering and Technology
Copyright. If accepted, the copy of record will be available at IET Digital
Librar
The interplay of microscopic and mesoscopic structure in complex networks
Not all nodes in a network are created equal. Differences and similarities
exist at both individual node and group levels. Disentangling single node from
group properties is crucial for network modeling and structural inference.
Based on unbiased generative probabilistic exponential random graph models and
employing distributive message passing techniques, we present an efficient
algorithm that allows one to separate the contributions of individual nodes and
groups of nodes to the network structure. This leads to improved detection
accuracy of latent class structure in real world data sets compared to models
that focus on group structure alone. Furthermore, the inclusion of hitherto
neglected group specific effects in models used to assess the statistical
significance of small subgraph (motif) distributions in networks may be
sufficient to explain most of the observed statistics. We show the predictive
power of such generative models in forecasting putative gene-disease
associations in the Online Mendelian Inheritance in Man (OMIM) database. The
approach is suitable for both directed and undirected uni-partite as well as
for bipartite networks
Sex differences in intimate relationships
Social networks have turned out to be of fundamental importance both for our
understanding human sociality and for the design of digital communication
technology. However, social networks are themselves based on dyadic
relationships and we have little understanding of the dynamics of close
relationships and how these change over time. Evolutionary theory suggests
that, even in monogamous mating systems, the pattern of investment in close
relationships should vary across the lifespan when post-weaning investment
plays an important role in maximising fitness. Mobile phone data sets provide
us with a unique window into the structure of relationships and the way these
change across the lifespan. We here use data from a large national mobile phone
dataset to demonstrate striking sex differences in the pattern in the
gender-bias of preferred relationships that reflect the way the reproductive
investment strategies of the two sexes change across the lifespan: these
differences mainly reflect women's shifting patterns of investment in
reproduction and parental care. These results suggest that human social
strategies may have more complex dynamics than we have tended to assume and a
life-history perspective may be crucial for understanding them.Comment: 5 pages, 3 figures, contains electronic supplementary materia
Close relationships: A study of mobile communication records
Mobile phone communication as digital service generates ever-increasing
datasets of human communication actions, which in turn allow us to investigate
the structure and evolution of social interactions and their networks. These
datasets can be used to study the structuring of such egocentric networks with
respect to the strength of the relationships by assuming direct dependence of
the communication intensity on the strength of the social tie. Recently we have
discovered that there are significant differences between the first and further
"best friends" from the point of view of age and gender preferences. Here we
introduce a control parameter based on the statistics of
communication with the first and second "best friend" and use it to filter the
data. We find that when is decreased the identification of the
"best friend" becomes less ambiguous and the earlier observed effects get
stronger, thus corroborating them.Comment: 11 pages, 7 figure
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