53,366 research outputs found
Viewpoint Discovery and Understanding in Social Networks
The Web has evolved to a dominant platform where everyone has the opportunity
to express their opinions, to interact with other users, and to debate on
emerging events happening around the world. On the one hand, this has enabled
the presence of different viewpoints and opinions about a - usually
controversial - topic (like Brexit), but at the same time, it has led to
phenomena like media bias, echo chambers and filter bubbles, where users are
exposed to only one point of view on the same topic. Therefore, there is the
need for methods that are able to detect and explain the different viewpoints.
In this paper, we propose a graph partitioning method that exploits social
interactions to enable the discovery of different communities (representing
different viewpoints) discussing about a controversial topic in a social
network like Twitter. To explain the discovered viewpoints, we describe a
method, called Iterative Rank Difference (IRD), which allows detecting
descriptive terms that characterize the different viewpoints as well as
understanding how a specific term is related to a viewpoint (by detecting other
related descriptive terms). The results of an experimental evaluation showed
that our approach outperforms state-of-the-art methods on viewpoint discovery,
while a qualitative analysis of the proposed IRD method on three different
controversial topics showed that IRD provides comprehensive and deep
representations of the different viewpoints
Latent Space Model for Multi-Modal Social Data
With the emergence of social networking services, researchers enjoy the
increasing availability of large-scale heterogenous datasets capturing online
user interactions and behaviors. Traditional analysis of techno-social systems
data has focused mainly on describing either the dynamics of social
interactions, or the attributes and behaviors of the users. However,
overwhelming empirical evidence suggests that the two dimensions affect one
another, and therefore they should be jointly modeled and analyzed in a
multi-modal framework. The benefits of such an approach include the ability to
build better predictive models, leveraging social network information as well
as user behavioral signals. To this purpose, here we propose the Constrained
Latent Space Model (CLSM), a generalized framework that combines Mixed
Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA)
incorporating a constraint that forces the latent space to concurrently
describe the multiple data modalities. We derive an efficient inference
algorithm based on Variational Expectation Maximization that has a
computational cost linear in the size of the network, thus making it feasible
to analyze massive social datasets. We validate the proposed framework on two
problems: prediction of social interactions from user attributes and behaviors,
and behavior prediction exploiting network information. We perform experiments
with a variety of multi-modal social systems, spanning location-based social
networks (Gowalla), social media services (Instagram, Orkut), e-commerce and
review sites (Amazon, Ciao), and finally citation networks (Cora). The results
indicate significant improvement in prediction accuracy over state of the art
methods, and demonstrate the flexibility of the proposed approach for
addressing a variety of different learning problems commonly occurring with
multi-modal social data.Comment: 12 pages, 7 figures, 2 table
Community Detection in Networks with Node Attributes
Community detection algorithms are fundamental tools that allow us to uncover
organizational principles in networks. When detecting communities, there are
two possible sources of information one can use: the network structure, and the
features and attributes of nodes. Even though communities form around nodes
that have common edges and common attributes, typically, algorithms have only
focused on one of these two data modalities: community detection algorithms
traditionally focus only on the network structure, while clustering algorithms
mostly consider only node attributes. In this paper, we develop Communities
from Edge Structure and Node Attributes (CESNA), an accurate and scalable
algorithm for detecting overlapping communities in networks with node
attributes. CESNA statistically models the interaction between the network
structure and the node attributes, which leads to more accurate community
detection as well as improved robustness in the presence of noise in the
network structure. CESNA has a linear runtime in the network size and is able
to process networks an order of magnitude larger than comparable approaches.
Last, CESNA also helps with the interpretation of detected communities by
finding relevant node attributes for each community.Comment: Published in the proceedings of IEEE ICDM '1
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