Graph Learning with Distributional Edge Layouts

Graph Neural Networks (GNNs) learn from graph-structured data by passing local messages between neighboring nodes along edges on certain topological layouts. Typically, these topological layouts in modern GNNs are deterministically computed (e.g., attention-based GNNs) or locally sampled (e.g., GraphSage) under heuristic assumptions. In this paper, we for the first time pose that these layouts can be globally sampled via Langevin dynamics following Boltzmann distribution equipped with explicit physical energy, leading to higher feasibility in the physical world. We argue that such a collection of sampled/optimized layouts can capture the wide energy distribution and bring extra expressivity on top of WL-test, therefore easing downstream tasks. As such, we propose Distributional Edge Layouts (DELs) to serve as a complement to a variety of GNNs. DEL is a pre-processing strategy independent of subsequent GNN variants, thus being highly flexible. Experimental results demonstrate that DELs consistently and substantially improve a series of GNN baselines, achieving state-of-the-art performance on multiple datasets.

Boosting Graph Pooling with Persistent Homology

Recently, there has been an emerging trend to integrate persistent homology (PH) into graph neural networks (GNNs) to enrich expressive power. However, naively plugging PH features into GNN layers always results in marginal improvement with low interpretability. In this paper, we investigate a novel mechanism for injecting global topological invariance into pooling layers using PH, motivated by the observation that filtration operation in PH naturally aligns graph pooling in a cut-off manner. In this fashion, message passing in the coarsened graph acts along persistent pooled topology, leading to improved performance. Experimentally, we apply our mechanism to a collection of graph pooling methods and observe consistent and substantial performance gain over several popular datasets, demonstrating its wide applicability and flexibility.

Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation

Graph contrastive learning (GCL) has emerged as a pivotal technique in the domain of graph representation learning. A crucial aspect of effective GCL is the caliber of generated positive and negative samples, which is intrinsically dictated by their resemblance to the original data. Nevertheless, precise control over similarity during sample generation presents a formidable challenge, often impeding the effective discovery of representative graph patterns. To address this challenge, we propose an innovative framework: Adversarial Curriculum Graph Contrastive Learning (ACGCL), which capitalizes on the merits of pair-wise augmentation to engender graph-level positive and negative samples with controllable similarity, alongside subgraph contrastive learning to discern effective graph patterns therein. Within the ACGCL framework, we have devised a novel adversarial curriculum training methodology that facilitates progressive learning by sequentially increasing the difficulty of distinguishing the generated samples. Notably, this approach transcends the prevalent sparsity issue inherent in conventional curriculum learning strategies by adaptively concentrating on more challenging training data. Finally, a comprehensive assessment of ACGCL is conducted through extensive experiments on six well-known benchmark datasets, wherein ACGCL conspicuously surpasses a set of state-of-the-art baselines.

Embedding in Recommender Systems: A Survey

Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that coverts the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors and can enhance the recommendation performance. Applying embedding techniques captures complex entity relationships and has spurred substantial research. In this survey, we provide an overview of the recent literature on embedding techniques in recommender systems. This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques. Collaborative filtering generates embeddings capturing user-item preferences, excelling in sparse data. Self-supervised methods leverage contrastive or generative learning for various tasks. Graph-based techniques like node2vec exploit complex relationships in network-rich environments. Addressing the scalability challenges inherent to embedding methods, our survey delves into innovative directions within the field of recommendation systems. These directions aim to enhance performance and reduce computational complexity, paving the way for improved recommender systems. Among these innovative approaches, we will introduce Auto Machine Learning (AutoML), hash techniques, and quantization techniques in this survey. We discuss various architectures and techniques and highlight the challenges and future directions in these aspects. This survey aims to provide a comprehensive overview of the state-of-the-art in this rapidly evolving field and serve as a useful resource for researchers and practitioners working in the area of recommender systems.