3 research outputs found
Understanding Graph Data Through Deep Learning Lens
Deep neural network models have established themselves as an unparalleled force in the domains
of vision, speech and text processing applications in recent years. However, graphs have formed a
significant component of data analytics including applications in Internet of Things, social networks,
pharmaceuticals and bioinformatics. An important characteristic of these deep learning techniques
is their ability to learn the important features which are necessary to excel at a given task, unlike
traditional machine learning algorithms which are dependent on handcrafted features. However,
there have been comparatively fewer e�orts in deep learning to directly work on graph inputs.
Various real-world problems can be easily solved by posing them as a graph analysis problem.
Considering the direct impact of the success of graph analysis on business outcomes, importance of
studying these complex graph data has increased exponentially over the years.
In this thesis, we address three contributions towards understanding graph data: (i) The first
contribution seeks to find anomalies in graphs using graphical models; (ii) The second contribution
uses deep learning with spatio-temporal random walks to learn representations of graph trajectories
(paths) and shows great promise on standard graph datasets; and (iii) The third contribution seeks
to propose a novel deep neural network that implicitly models attention to allow for interpretation
of graph classification.
STWalk: Learning Trajectory Representations in Temporal Graphs
Analyzing the temporal behavior of nodes in time-varying graphs is useful for
many applications such as targeted advertising, community evolution and outlier
detection. In this paper, we present a novel approach, STWalk, for learning
trajectory representations of nodes in temporal graphs. The proposed framework
makes use of structural properties of graphs at current and previous time-steps
to learn effective node trajectory representations. STWalk performs random
walks on a graph at a given time step (called space-walk) as well as on graphs
from past time-steps (called time-walk) to capture the spatio-temporal behavior
of nodes. We propose two variants of STWalk to learn trajectory
representations. In one algorithm, we perform space-walk and time-walk as part
of a single step. In the other variant, we perform space-walk and time-walk
separately and combine the learned representations to get the final trajectory
embedding. Extensive experiments on three real-world temporal graph datasets
validate the effectiveness of the learned representations when compared to
three baseline methods. We also show the goodness of the learned trajectory
embeddings for change point detection, as well as demonstrate that arithmetic
operations on these trajectory representations yield interesting and
interpretable results.Comment: 10 pages, 5 figures, 2 table