Despite the initial success of node injection attacks, we find that the injected nodes by existing methods are easy to be distinguished from the original normal nodes by defense methods and limiting their attack performance in practice.
The invariance may degrade the representation learning ability of the discriminator, thereby affecting the generative modeling performance of the generator.
The expressive power of message passing GNNs is upper-bounded by Weisfeiler-Lehman (WL) test.
In this paper, we focus on an extremely limited scenario of single node injection evasion attack, i. e., the attacker is only allowed to inject one single node during the test phase to hurt GNN's performance.
Signed bipartite networks are different from classical signed networks, which contain two different node sets and signed links between two node sets.
Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively.
Ranked #1 on Conditional Image Generation on Tiny ImageNet
INMO generates the inductive embeddings for users (items) by characterizing their interactions with some template items (template users), instead of employing an embedding lookup table.
Recently, transformation-based self-supervised learning has been applied to generative adversarial networks (GANs) to mitigate catastrophic forgetting in the discriminator by introducing a stationary learning environment.
The framework can partition data into custom designed time buckets to capture the interactions among information aggregated in different time buckets.
For a target node, diverse sampling offers it diverse neighborhoods, i. e., rooted sub-graphs, and the representation of target node is finally obtained via aggregating the representation of diverse neighborhoods obtained using any GNN model.
In this work, we develop practical user scheduling algorithms for downlink bursty traffic with emphasis on user fairness.
Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models.
In this paper, we consider the problem of network-aware popularity prediction, leveraging both early adopters and social networks for popularity prediction.
In this paper, we address attributed network embedding from a novel perspective, i. e., learning node context representation for each node via modeling its attributed local subgraph.
We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform.
Ranked #47 on Node Classification on Pubmed
In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process — a widely-used generative process to model information cascade.