17 research outputs found
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
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
Contrastive graph node clustering via learnable data augmentation is a hot
research spot in the field of unsupervised graph learning. The existing methods
learn the sampling distribution of a pre-defined augmentation to generate
data-driven augmentations automatically. Although promising clustering
performance has been achieved, we observe that these strategies still rely on
pre-defined augmentations, the semantics of the augmented graph can easily
drift. The reliability of the augmented view semantics for contrastive learning
can not be guaranteed, thus limiting the model performance. To address these
problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable
AugmenTation (COVERT). Specifically, in our method, the data augmentations are
processed by the proposed reversible perturb-recover network. It distills
reliable semantic information by recovering the perturbed latent embeddings.
Moreover, to further guarantee the reliability of semantics, a novel semantic
loss is presented to constrain the network via quantifying the perturbation and
recovery. Lastly, a label-matching mechanism is designed to guide the model by
clustering information through aligning the semantic labels and the selected
high-confidence clustering pseudo labels. Extensive experimental results on
seven datasets demonstrate the effectiveness of the proposed method. We release
the code and appendix of CONVERT at https://github.com/xihongyang1999/CONVERT
on GitHub
Greedy PIG: Adaptive Integrated Gradients
Deep learning has become the standard approach for most machine learning
tasks. While its impact is undeniable, interpreting the predictions of deep
learning models from a human perspective remains a challenge. In contrast to
model training, model interpretability is harder to quantify and pose as an
explicit optimization problem. Inspired by the AUC softmax information curve
(AUC SIC) metric for evaluating feature attribution methods, we propose a
unified discrete optimization framework for feature attribution and feature
selection based on subset selection. This leads to a natural adaptive
generalization of the path integrated gradients (PIG) method for feature
attribution, which we call Greedy PIG. We demonstrate the success of Greedy PIG
on a wide variety of tasks, including image feature attribution, graph
compression/explanation, and post-hoc feature selection on tabular data. Our
results show that introducing adaptivity is a powerful and versatile method for
making attribution methods more powerful