Hi everyone,
Please join us for our next DM-Meeting.
****DM-Meeting Spring 2016****
WHO: Bijaya Adhikari
WHEN: Monday, Feb. 27, 2017 @ 4:00 pm
WHERE: Torg 3160 A
WHAT: Bijaya will talk about the following two papers:
*1) Scalable Temporal Latent Space Inference for Link Prediction in
Dynamic Social Networks (TKDE 2016)*
*Abstract*: We propose a temporal latent space model for link prediction in
dynamic social networks, where the goal is to predict links over time based
on a sequence of previous graph snapshots. The model assumes that each user
lies in an unobserved latent space, and interactions are more likely to
occur between similar users in the latent space representation. In
addition, the model allows each user to gradually move its position in the
latent space as the network structure evolves over time. We present a
global optimization algorithm to effectively infer the temporal latent
space. Two alternative optimization algorithms with local and incremental
updates are also proposed, allowing the model to scale to larger networks
without compromising prediction accuracy. Empirically, we demonstrate that
our model, when evaluated on a number of real-world dynamic networks,
significantly outperforms existing approaches for temporal link prediction
in terms of both scalability and predictive power.
*2) Temporally Factorized Network Modeling for Evolutionary Network
Analysis (WSDM 2017)*
*Abstract*: The problem of evolutionary network analysis has gained
increasing attention in recent years, because of an increasing number of
networks, which are encountered in temporal settings. For example, social
networks, communication networks, and information networks continuously
evolve over time, and it is desirable to learn interesting trends about how
the network structure evolves over time, and in terms of other interesting
trends. One challenging aspect of networks is that they are inherently
resistant to parametric modeling, which allows us to truly express the
edges in the network as functions of time. This is because, unlike
multidimensional data, the edges in the network reflect interactions among
nodes, and it is difficult to independently model the edge as a function of
time, without taking into account its correlations and interactions with
neighboring edges. Fortunately, we show that it is indeed possible to
achieve this goal with the use of a matrix factorization, in which the
entries are parameterized by time. This approach allows us to represent the
edge structure of the network purely as a function of time, and predict the
evolution of the network over time. This opens the possibility of using the
approach for a wide variety of temporal network analysis problems, such as
predicting future trends in structures, predicting links, and node-centric
anomaly/event detection. This flexibility is because of the general way in
which the approach allows us to express the structure of the network as a
function of time. We present a number of experimental results on a number
of temporal data sets showing the effectiveness of the approach.
Best regards,
Sorour
