18,486 research outputs found
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
TimeLighting: Guidance-enhanced Exploration of 2D Projections of Temporal Graphs
In temporal (or event-based) networks, time is a continuous axis, with
real-valued time coordinates for each node and edge. Computing a layout for
such graphs means embedding the node trajectories and edge surfaces over time
in a 2D + t space, known as the space-time cube. Currently, these space-time
cube layouts are visualized through animation or by slicing the cube at regular
intervals. However, both techniques present problems ranging from sub-par
performance on some tasks to loss of precision. In this paper, we present
TimeLighting, a novel visual analytics approach to visualize and explore
temporal graphs embedded in the space-time cube. Our interactive approach
highlights the node trajectories and their mobility over time, visualizes node
"aging", and provides guidance to support users during exploration. We evaluate
our approach through two case studies, showing the system's efficacy in
identifying temporal patterns and the role of the guidance features in the
exploration process.Comment: Appears in the Proceedings of the 31st International Symposium on
Graph Drawing and Network Visualization (GD 2023
- …