Chickenpox Cases in Hungary: A Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks

Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing. Newly proposed graph neural network architectures are repetitively evaluated on standard tasks such as traffic or weather forecasting. In this paper, we propose the Chickenpox Cases in Hungary dataset as a new dataset for comparing graph neural network architectures. Our time series analysis and forecasting experiments demonstrate that the Chickenpox Cases in Hungary dataset is adequate for comparing the predictive performance and forecasting capabilities of novel recurrent graph neural network architectures

Novel Techniques Using Graph Neural Networks (GNNS) for Anomaly Detection

This paper explores 2 new mechanisms that leverage graphs for anomaly detection. The novelty in approach one is to leverage the global attention capability of transformer architecture using a Graph Attention Network (GAT) with Chebyshev Laplacian for representation. This method leverages the GAT to learn attention weights for the graph features obtained through Chebyshev expansion of the Laplacian. This method focuses on capturing higher-order graph features with reduced computational complexity and utilizing attention mechanisms for improved feature relevance in detecting anomalies. The second approach leverages Fisher information to find anomalous graphs with ChebNet module for graph analysis. The ChebNet module allows for deep learning on graphs, capturing complex patterns and relationships that can help in detecting fraud more accurately. Using Fisher information improves model interpretability while ChebNet modules help leverage spectral properties

NCGNN: Node-level Capsule Graph Neural Network

Message passing has evolved as an effective tool for designing Graph Neural Networks (GNNs). However, most existing works naively sum or average all the neighboring features to update node representations, which suffers from the following limitations: (1) lack of interpretability to identify crucial node features for GNN's prediction; (2) over-smoothing issue where repeated averaging aggregates excessive noise, making features of nodes in different classes over-mixed and thus indistinguishable. In this paper, we propose the Node-level Capsule Graph Neural Network (NCGNN) to address these issues with an improved message passing scheme. Specifically, NCGNN represents nodes as groups of capsules, in which each capsule extracts distinctive features of its corresponding node. For each node-level capsule, a novel dynamic routing procedure is developed to adaptively select appropriate capsules for aggregation from a subgraph identified by the designed graph filter. Consequently, as only the advantageous capsules are aggregated and harmful noise is restrained, over-mixing features of interacting nodes in different classes tends to be avoided to relieve the over-smoothing issue. Furthermore, since the graph filter and the dynamic routing identify a subgraph and a subset of node features that are most influential for the prediction of the model, NCGNN is inherently interpretable and exempt from complex post-hoc explanations. Extensive experiments on six node classification benchmarks demonstrate that NCGNN can well address the over-smoothing issue and outperforms the state of the arts by producing better node embeddings for classification

Multi-scale attributed node embedding

We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE). Capturing attribute-neighborhood relationships over multiple scales is useful for a diverse range of applications, including latent feature identification across disconnected networks with similar attributes. We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings. Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social networks and web graphs.Comment: Published in the Journal of Complex Network

GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner

Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data. Among both sets of graph SSL techniques, the masked graph autoencoders (e.g., GraphMAE)--one type of generative method--have recently produced promising results. The idea behind this is to reconstruct the node features (or structures)--that are randomly masked from the input--with the autoencoder architecture. However, the performance of masked feature reconstruction naturally relies on the discriminability of the input features and is usually vulnerable to disturbance in the features. In this paper, we present a masked self-supervised learning framework GraphMAE2 with the goal of overcoming this issue. The idea is to impose regularization on feature reconstruction for graph SSL. Specifically, we design the strategies of multi-view random re-mask decoding and latent representation prediction to regularize the feature reconstruction. The multi-view random re-mask decoding is to introduce randomness into reconstruction in the feature space, while the latent representation prediction is to enforce the reconstruction in the embedding space. Extensive experiments show that GraphMAE2 can consistently generate top results on various public datasets, including at least 2.45% improvements over state-of-the-art baselines on ogbn-Papers100M with 111M nodes and 1.6B edges.Comment: Accepted to WWW'2