14 research outputs found
Tracing Network Evolution Using the PARAFAC2 Model
Characterizing time-evolving networks is a challenging task, but it is
crucial for understanding the dynamic behavior of complex systems such as the
brain. For instance, how spatial networks of functional connectivity in the
brain evolve during a task is not well-understood. A traditional approach in
neuroimaging data analysis is to make simplifications through the assumption of
static spatial networks. In this paper, without assuming static networks in
time and/or space, we arrange the temporal data as a higher-order tensor and
use a tensor factorization model called PARAFAC2 to capture underlying patterns
(spatial networks) in time-evolving data and their evolution. Numerical
experiments on simulated data demonstrate that PARAFAC2 can successfully reveal
the underlying networks and their dynamics. We also show the promising
performance of the model in terms of tracing the evolution of task-related
functional connectivity in the brain through the analysis of functional
magnetic resonance imaging data.Comment: 5 pages, 5 figures, conferenc
A Dynamic Embedding Model of the Media Landscape
Information about world events is disseminated through a wide variety of news
channels, each with specific considerations in the choice of their reporting.
Although the multiplicity of these outlets should ensure a variety of
viewpoints, recent reports suggest that the rising concentration of media
ownership may void this assumption. This observation motivates the study of the
impact of ownership on the global media landscape and its influence on the
coverage the actual viewer receives. To this end, the selection of reported
events has been shown to be informative about the high-level structure of the
news ecosystem. However, existing methods only provide a static view into an
inherently dynamic system, providing underperforming statistical models and
hindering our understanding of the media landscape as a whole.
In this work, we present a dynamic embedding method that learns to capture
the decision process of individual news sources in their selection of reported
events while also enabling the systematic detection of large-scale
transformations in the media landscape over prolonged periods of time. In an
experiment covering over 580M real-world event mentions, we show our approach
to outperform static embedding methods in predictive terms. We demonstrate the
potential of the method for news monitoring applications and investigative
journalism by shedding light on important changes in programming induced by
mergers and acquisitions, policy changes, or network-wide content diffusion.
These findings offer evidence of strong content convergence trends inside large
broadcasting groups, influencing the news ecosystem in a time of increasing
media ownership concentration
Node Embedding over Temporal Graphs
In this work, we present a method for node embedding in temporal graphs. We
propose an algorithm that learns the evolution of a temporal graph's nodes and
edges over time and incorporates this dynamics in a temporal node embedding
framework for different graph prediction tasks. We present a joint loss
function that creates a temporal embedding of a node by learning to combine its
historical temporal embeddings, such that it optimizes per given task (e.g.,
link prediction). The algorithm is initialized using static node embeddings,
which are then aligned over the representations of a node at different time
points, and eventually adapted for the given task in a joint optimization. We
evaluate the effectiveness of our approach over a variety of temporal graphs
for the two fundamental tasks of temporal link prediction and multi-label node
classification, comparing to competitive baselines and algorithmic
alternatives. Our algorithm shows performance improvements across many of the
datasets and baselines and is found particularly effective for graphs that are
less cohesive, with a lower clustering coefficient
A General Framework for Evolving Network Analysis
Many efforts have been given to model physical systems involving a large number of interacting constituents. Such systems are commonly called complex networks for their complex behaviors demonstrated at the system, global level. As a network evolves its constituents (or nodes) and associated links would either increase or decrease or both. It is a challenge to extract the specifics that underlie the evolution of a network or indicate the addition and/or removal of links in time. Many evolving network analysis algorithms are available. However, the majority of these algorithms assume network evolution can be fully comprehended using a snapshot of the network as it progresses. Without retaining the time information would inevitably obscure the dynamics of the evolving network and misinterpret its behaviors. A general framework viable for describing evolving networks is developed in the thesis. The framework incorporates the two-dimensional discrete wavelet transform (2DWT) with the tensor factorization method to extract features indicative of the development of a network in the time-frequency domain. The general framework model is evaluated against six benchmark algorithms using five different real-world evolving network datasets to demonstrate its feasibility in achieving a high level of link prediction
Three Facets of Online Political Networks: Communities, Antagonisms, and Polarization
abstract: Millions of users leave digital traces of their political engagements on social media platforms every day. Users form networks of interactions, produce textual content, like and share each others' content. This creates an invaluable opportunity to better understand the political engagements of internet users. In this proposal, I present three algorithmic solutions to three facets of online political networks; namely, detection of communities, antagonisms and the impact of certain types of accounts on political polarization. First, I develop a multi-view community detection algorithm to find politically pure communities. I find that word usage among other content types (i.e. hashtags, URLs) complement user interactions the best in accurately detecting communities.
Second, I focus on detecting negative linkages between politically motivated social media users. Major social media platforms do not facilitate their users with built-in negative interaction options. However, many political network analysis tasks rely on not only positive but also negative linkages. Here, I present the SocLSFact framework to detect negative linkages among social media users. It utilizes three pieces of information; sentiment cues of textual interactions, positive interactions, and socially balanced triads. I evaluate the contribution of each three aspects in negative link detection performance on multiple tasks.
Third, I propose an experimental setup that quantifies the polarization impact of automated accounts on Twitter retweet networks. I focus on a dataset of tragic Parkland shooting event and its aftermath. I show that when automated accounts are removed from the retweet network the network polarization decrease significantly, while a same number of accounts to the automated accounts are removed randomly the difference is not significant. I also find that prominent predictors of engagement of automatically generated content is not very different than what previous studies point out in general engaging content on social media. Last but not least, I identify accounts which self-disclose their automated nature in their profile by using expressions such as bot, chat-bot, or robot. I find that human engagement to self-disclosing accounts compared to non-disclosing automated accounts is much smaller. This observational finding can motivate further efforts into automated account detection research to prevent their unintended impact.Dissertation/ThesisDoctoral Dissertation Computer Science 201