Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions

Most graph neural networks (GNNs) rely on the message passing paradigm to propagate node features and build interactions. Recent works point out that different graph learning tasks require different ranges of interactions between nodes. To investigate its underlying mechanism, we explore the capacity of GNNs to capture pairwise interactions between nodes under contexts with different complexities, especially for their graph-level and node-level applications in scientific domains like biochemistry and physics. When formulating pairwise interactions, we study two common graph construction methods in scientific domains, i.e., \emph{K-nearest neighbor} (KNN) graphs and \emph{fully-connected} (FC) graphs. Furthermore, we demonstrate that the inductive bias introduced by KNN-graphs and FC-graphs hinders GNNs to learn the most informative order of interactions. {Such a phenomenon is broadly shared by several GNNs for different graph learning tasks and forbids GNNs to achieve the global minimum loss, so we name it a \emph{representation bottleneck}.} To overcome that, we propose a novel graph rewiring approach based on the pairwise interaction strengths to dynamically adjust the reception fields of each node. Extensive experiments in molecular property prediction and dynamic system forecast prove the superiority of our method over state-of-the-art GNN baselines. More importantly, this paper provides a reasonable explanation of why subgraphs play an important role in the determination of graph properties.

Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem

We introduce the task of implicit offensive text detection in dialogues, where a statement may have either an offensive or non-offensive interpretation, depending on the listener and context. We argue that reasoning is crucial for understanding this broader class of offensive utterances and release SLIGHT, a dataset to support research on this task. Experiments using the data show that state-of-the-art methods of offense detection perform poorly when asked to detect implicitly offensive statements, achieving only ${\sim} 11\%$ accuracy. In contrast to existing offensive text detection datasets, SLIGHT features human-annotated chains of reasoning which describe the mental process by which an offensive interpretation can be reached from each ambiguous statement. We explore the potential for a multi-hop reasoning approach by utilizing existing entailment models to score the probability of these chains and show that even naive reasoning models can yield improved performance in most situations. Furthermore, analysis of the chains provides insight into the human interpretation process and emphasizes the importance of incorporating additional commonsense knowledge.

A Score-based Geometric Model for Molecular Dynamics Simulations

Molecular dynamics (MD) has long been the \emph{de facto} choice for modeling complex atomistic systems from first principles, and recently deep learning become a popular way to accelerate it. Notwithstanding, preceding approaches depend on intermediate variables such as the potential energy or force fields to update atomic positions, which requires additional computations to perform back-propagation. To waive this requirement, we propose a novel model called ScoreMD by directly estimating the gradient of the log density of molecular conformations. Moreover, we analyze that diffusion processes highly accord with the principle of enhanced sampling in MD simulations, and is therefore a perfect match to our sequential conformation generation task. That is, ScoreMD perturbs the molecular structure with a conditional noise depending on atomic accelerations and employs conformations at previous timeframes as the prior distribution for sampling. Another challenge of modeling such a conformation generation process is that the molecule is kinetic instead of static, which no prior studies strictly consider. To solve this challenge, we introduce a equivariant geometric Transformer as a score function in the diffusion process to calculate the corresponding gradient. It incorporates the directions and velocities of atomic motions via 3D spherical Fourier-Bessel representations. With multiple architectural improvements, we outperforms state-of-the-art baselines on MD17 and isomers of C7O2H10. This research provides new insights into the acceleration of new material and drug discovery.

StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts

Inferring spatial relations in natural language is a crucial ability an intelligent system should possess. The bAbI dataset tries to capture tasks relevant to this domain (task 17 and 19). However, these tasks have several limitations. Most importantly, they are limited to fixed expressions, they are limited in the number of reasoning steps required to solve them, and they fail to test the robustness of models to input that contains irrelevant or redundant information. In this paper, we present a new Question-Answering dataset called StepGame for robust multi-hop spatial reasoning in texts. Our experiments demonstrate that state-of-the-art models on the bAbI dataset struggle on the StepGame dataset. Moreover, we propose a Tensor-Product based Memory-Augmented Neural Network (TP-MANN) specialized for spatial reasoning tasks. Experimental results on both datasets show that our model outperforms all the baselines with superior generalization and robustness performance.

Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking

Dialogue State Tracking (DST) aims to keep track of users' intentions during the course of a conversation. In DST, modelling the relations among domains and slots is still an under-studied problem. Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains. To address these issues, we propose a novel \textbf{D}ynamic \textbf{S}chema \textbf{G}raph \textbf{F}usion \textbf{Net}work (\textbf{DSGFNet}), which generates a dynamic schema graph to explicitly fuse the prior slot-domain membership relations and dialogue-aware dynamic slot relations. It also uses the schemata to facilitate knowledge transfer to new domains. DSGFNet consists of a dialogue utterance encoder, a schema graph encoder, a dialogue-aware schema graph evolving network, and a schema graph enhanced dialogue state decoder. Empirical results on benchmark datasets (i.e., SGD, MultiWOZ2.1, and MultiWOZ2.2), show that DSGFNet outperforms existing methods.

Hybrid Routing Transformer for Zero-Shot Learning

Zero-shot learning (ZSL) aims to learn models that can recognize unseen image semantics based on the training of data with seen semantics. Recent studies either leverage the global image features or mine discriminative local patch features to associate the extracted visual features to the semantic attributes. However, due to the lack of the necessary top-down guidance and semantic alignment for ensuring the model attending to the real attribute-correlation regions, these methods still encounter a significant semantic gap between the visual modality and the attribute modality, which makes their prediction on unseen semantics unreliable. To solve this problem, this paper establishes a novel transformer encoder-decoder model, called hybrid routing transformer (HRT). In HRT encoder, we embed an active attention, which is constructed by both the bottom-up and the top-down dynamic routing pathways to generate the attribute-aligned visual feature. While in HRT decoder, we use static routing to calculate the correlation among the attribute-aligned visual features, the corresponding attribute semantics, and the class attribute vectors to generate the final class label predictions. This design makes the presented transformer model a hybrid of 1) top-down and bottom-up attention pathways and 2) dynamic and static routing pathways. Comprehensive experiments on three widely-used benchmark datasets, namely CUB, SUN, and AWA2, are conducted. The obtained experimental results demonstrate the effectiveness of the proposed method.

Deep Reinforcement Learning for Entity Alignment

Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies. Although great promise they can offer, there are still several limitations. The most notable is that they identify the aligned entities based on cosine similarity, ignoring the semantics underlying the embeddings themselves. Furthermore, these methods are shortsighted, heuristically selecting the closest entity as the target and allowing multiple entities to match the same candidate. To address these limitations, we model entity alignment as a sequential decision-making task, in which an agent sequentially decides whether two entities are matched or mismatched based on their representation vectors. The proposed reinforcement learning (RL)-based entity alignment framework can be flexibly adapted to most embedding-based EA methods. The experimental results demonstrate that it consistently advances the performance of several state-of-the-art methods, with a maximum improvement of 31.1% on Hits@1.

Unleashing the Power of Transformer for Graphs

Despite recent successes in natural language processing and computer vision, Transformer suffers from the scalability problem when dealing with graphs. The computational complexity is unacceptable for large-scale graphs, e.g., knowledge graphs. One solution is to consider only the near neighbors, which, however, will lose the key merit of Transformer to attend to the elements at any distance. In this paper, we propose a new Transformer architecture, named dual-encoding Transformer (DET). DET has a structural encoder to aggregate information from connected neighbors and a semantic encoder to focus on semantically useful distant nodes. In comparison with resorting to multi-hop neighbors, DET seeks the desired distant neighbors via self-supervised training. We further find these two encoders can be incorporated to boost each others' performance. Our experiments demonstrate DET has achieved superior performance compared to the respective state-of-the-art methods in dealing with molecules, networks and knowledge graphs with various sizes.

Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer

Machine learning, especially deep learning, has greatly advanced molecular studies in the biochemical domain. Most typically, modeling for most molecular tasks have converged to several paradigms. For example, we usually adopt the prediction paradigm to solve tasks of molecular property prediction. To improve the generation and interpretability of purely data-driven models, researchers have incorporated biochemical domain knowledge into these models for molecular studies. This knowledge incorporation has led to a rising trend of paradigm transfer, which is solving one molecular learning task by reformulating it as another one. In this paper, we present a literature review towards knowledge-informed molecular learning in perspective of paradigm transfer, where we categorize the paradigms, review their methods and analyze how domain knowledge contributes. Furthermore, we summarize the trends and point out interesting future directions for molecular learning.

Prompt-Guided Injection of Conformation to Pre-trained Protein Model

Pre-trained protein models (PTPMs) represent a protein with one fixed embedding and thus are not capable for diverse tasks. For example, protein structures can shift, namely protein folding, between several conformations in various biological processes. To enable PTPMs to produce task-aware representations, we propose to learn interpretable, pluggable and extensible protein prompts as a way of injecting task-related knowledge into PTPMs. In this regard, prior PTPM optimization with the masked language modeling task can be interpreted as learning a sequence prompt (Seq prompt) that enables PTPMs to capture the sequential dependency between amino acids. To incorporate conformational knowledge to PTPMs, we propose an interaction-conformation prompt (IC prompt) that is learned through back-propagation with the protein-protein interaction task. As an instantiation, we present a conformation-aware pre-trained protein model that learns both sequence and interaction-conformation prompts in a multi-task setting. We conduct comprehensive experiments on nine protein datasets. Results confirm our expectation that using the sequence prompt does not hurt PTPMs' performance on sequence-related tasks while incorporating the interaction-conformation prompt significantly improves PTPMs' performance on tasks where conformational knowledge counts. We also show the learned prompts can be combined and extended to deal with new complex tasks.