35 research outputs found
Gravity-Inspired Graph Autoencoders for Directed Link Prediction
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged
as powerful node embedding methods. In particular, graph AE and VAE were
successfully leveraged to tackle the challenging link prediction problem,
aiming at figuring out whether some pairs of nodes from a graph are connected
by unobserved edges. However, these models focus on undirected graphs and
therefore ignore the potential direction of the link, which is limiting for
numerous real-life applications. In this paper, we extend the graph AE and VAE
frameworks to address link prediction in directed graphs. We present a new
gravity-inspired decoder scheme that can effectively reconstruct directed
graphs from a node embedding. We empirically evaluate our method on three
different directed link prediction tasks, for which standard graph AE and VAE
perform poorly. We achieve competitive results on three real-world graphs,
outperforming several popular baselines.Comment: ACM International Conference on Information and Knowledge Management
(CIKM 2019
Adversarial Directed Graph Embedding
Node representation learning for directed graphs is critically important to
facilitate many graph mining tasks. To capture the directed edges between
nodes, existing methods mostly learn two embedding vectors for each node,
source vector and target vector. However, these methods learn the source and
target vectors separately. For the node with very low indegree or outdegree,
the corresponding target vector or source vector cannot be effectively learned.
In this paper, we propose a novel Directed Graph embedding framework based on
Generative Adversarial Network, called DGGAN. The main idea is to use
adversarial mechanisms to deploy a discriminator and two generators that
jointly learn each node's source and target vectors. For a given node, the two
generators are trained to generate its fake target and source neighbor nodes
from the same underlying distribution, and the discriminator aims to
distinguish whether a neighbor node is real or fake. The two generators are
formulated into a unified framework and could mutually reinforce each other to
learn more robust source and target vectors. Extensive experiments show that
DGGAN consistently and significantly outperforms existing state-of-the-art
methods across multiple graph mining tasks on directed graphs.Comment: 8 pages, 5 figure
Embedding Directed Graphs in Potential Fields Using FastMap-D
Embedding undirected graphs in a Euclidean space has many computational
benefits. FastMap is an efficient embedding algorithm that facilitates a
geometric interpretation of problems posed on undirected graphs. However,
Euclidean distances are inherently symmetric and, thus, Euclidean embeddings
cannot be used for directed graphs. In this paper, we present FastMap-D, an
efficient generalization of FastMap to directed graphs. FastMap-D embeds
vertices using a potential field to capture the asymmetry between the pairwise
distances in directed graphs. FastMap-D learns a potential function to define
the potential field using a machine learning module. In experiments on various
kinds of directed graphs, we demonstrate the advantage of FastMap-D over other
approaches.Comment: 9 pages, Published in Symposium on Combinatorial Search(SoCS-2020).
Erratum with updated Result