On the Robustness of Post-hoc GNN Explainers to Label Noise

Proposed as a solution to the inherent black-box limitations of graph neural networks (GNNs), post-hoc GNN explainers aim to provide precise and insightful explanations of the behaviours exhibited by trained GNNs. Despite their recent notable advancements in academic and industrial contexts, the robustness of post-hoc GNN explainers remains unexplored when confronted with label noise. To bridge this gap, we conduct a systematic empirical investigation to evaluate the efficacy of diverse post-hoc GNN explainers under varying degrees of label noise. Our results reveal several key insights: Firstly, post-hoc GNN explainers are susceptible to label perturbations. Secondly, even minor levels of label noise, inconsequential to GNN performance, harm the quality of generated explanations substantially. Lastly, we engage in a discourse regarding the progressive recovery of explanation effectiveness with escalating noise levels

An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

The outbreak of the COVID-19 leads to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out the distinctive characteristics of the Greater Region (GR) through conducting a data-driven exploratory study of Twitter COVID-19 information in the GR and related countries using machine learning and representation learning methods. We find that tweets volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 2020-01-22 to 2020-06-05, figuring out the main differences between GR and related countries

Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability

The integration of Artificial Intelligence (AI) into the field of drug discovery has been a growing area of interdisciplinary scientific research. However, conventional AI models are heavily limited in handling complex biomedical structures (such as 2D or 3D protein and molecule structures) and providing interpretations for outputs, which hinders their practical application. As of late, Graph Machine Learning (GML) has gained considerable attention for its exceptional ability to model graph-structured biomedical data and investigate their properties and functional relationships. Despite extensive efforts, GML methods still suffer from several deficiencies, such as the limited ability to handle supervision sparsity and provide interpretability in learning and inference processes, and their ineffectiveness in utilising relevant domain knowledge. In response, recent studies have proposed integrating external biomedical knowledge into the GML pipeline to realise more precise and interpretable drug discovery with limited training instances. However, a systematic definition for this burgeoning research direction is yet to be established. This survey presents a comprehensive overview of long-standing drug discovery principles, provides the foundational concepts and cutting-edge techniques for graph-structured data and knowledge databases, and formally summarises Knowledge-augmented Graph Machine Learning (KaGML) for drug discovery. A thorough review of related KaGML works, collected following a carefully designed search methodology, are organised into four categories following a novel-defined taxonomy. To facilitate research in this promptly emerging field, we also share collected practical resources that are valuable for intelligent drug discovery and provide an in-depth discussion of the potential avenues for future advancements

Leveraging Graph Machine Learning for Social Network Analysis

As a ubiquitous complex system in quotidian life around everyone, online social networks (OSNs) provide a rich source of information about billions of users worldwide. To some extent, OSNs have mirrored our real society: people perform a multitude of different activities in OSNs as they do in the offline world, such as establishing social relations, sharing life moments, and expressing opinions about various topics. Therefore, understanding OSNs is of immense importance. One key characteristic of human social behaviour in OSNs is their inter-relational nature, which can be represented as graphs. Due to sparsity and complex structure, analysing these graphs is quite challenging and expensive. Over the past several decades, many expert-designed approaches to graphs have been proposed with elegant theoretical properties and successfully addressed numerous practical problems. Nevertheless, most of them are either not data-driven or do not benefit from the rapidly growing scale of data. Recently, in the light of remarkable achievements of artificial intelligence, especially deep neural networks techniques, graph machine learning (GML) has emerged to provide us with novel perspectives to understanding and analysing graphs. However, the current efforts of GML are relatively immature and lack attention to specific scenarios and characteristics of OSNs. Based on the pros and cons of GML, this thesis discusses several aspects of how to build advanced approaches to better simplify and ameliorate OSN analytic tasks. Specifically: 1) Overcoming flat message-passing graph neural networks. One of the most widely pursued branches in GML research, graph neural networks (GNNs), follows a similar flat message-passing principle for representation learning. Precisely, information is iteratively passed between adjacent nodes along observed edges via non-linear transformation and aggregation functions. Its effectiveness has been widely proved; however, two limitations need to be tackled: (i) they are costly in encoding long-range information spanning the graph structure; (ii) they are failing to encode features in the high-order neighbourhood in the graphs as they only perform information aggregation across the observed edges in the original graph. To fill up the gap, we propose a novel hierarchical message-passing framework to facilitate the existing GNN mechanism. Following this idea, we design two practical implementations, i.e., HC-GNN and AdamGNN, to demonstrate the framework's superiority. 2) Extending graph machine learning to heterophilous graphs. The existing GML approaches implicitly hold a homophily assumption that nodes of the same class tend to be connected. However, previous expert studies have shown the enormous importance of addressing the heterophily scenario, where ``opposites attract'', is essential for network analysis and fairness study. We demonstrate the possibility of extending GML to heterophilous graphs by simplifying supervised node classification models on heterophilous graphs (CLP) and designing an unsupervised heterophilous graph representation learning model (Selene). 3) Online social network analysis with graph machine learning. As GML approaches have demonstrated significant effectiveness over general graph analytic tasks, we perform two practical OSN analysis projects to illustrate the possibility of employing GML in practice. Specifically, we propose a semantic image graph embedding (SiGraph) to improve OSN image recognition task with the associated hashtags semantics and a simple GNN-based neural link prediction framework (NeuLP) to boost the performance with tiny change. Keywords: Graph machine learning, Social network analysis, Graph neural networks, Hierarchical structure, Homophily/Heterophily graphs, Link prediction, Online image content understanding

Adaptive Multi-grained Graph Neural Networks

Graph Neural Networks (GNNs) have been increasingly deployed in a multitude of different applications that involve node-wise and graph-level tasks. The existing literature usually studies these questions independently while they are inherently correlated. We propose in this work a unified model, Adaptive Multi-grained GNN (AdamGNN), to learn node and graph level representation interactively. Compared with the existing GNN models and pooling methods, AdamGNN enhances node representation with multi-grained semantics and avoids node feature and graph structure information loss during pooling. More specifically, a differentiable pooling operator in AdamGNN is used to obtain a multi-grained structure that involves node-wise and meso/macro level semantic information. The unpooling and flyback aggregators in AdamGNN is to leverage the multi-grained semantics to enhance node representation. The updated node representation can further enrich the generated graph representation in the next iteration. Experimental results on twelve real-world graphs demonstrate the effectiveness of AdamGNN on multiple tasks, compared with several competing methods. In addition, the ablation and empirical studies confirm the effectiveness of different components in AdamGNN

Hierarchical Message-Passing Graph Neural Networks

Graph Neural Networks (GNNs) have become a promising approach to machine learning with graphs. Since existing GNN models are based on flat message-passing mechanisms, two limitations need to be tackled. One is costly in encoding global information on the graph topology. The other is failing to model meso- and macro-level semantics hidden in the graph, such as the knowledge of institutes and research areas in an academic collaboration network. To deal with these two issues, we propose a novel Hierarchical Message-Passing Graph Neural Networks framework. The main idea is to generate a hierarchical structure that re-organises all nodes in a graph into multi-level clusters, along with intra- and inter-level edge connections. The derived hierarchy not only creates shortcuts connecting far-away nodes so that global information can be efficiently accessed via message passing but also incorporates meso- and macro-level semantics into the learning of node embedding. We present the first model to implement this hierarchical message-passing mechanism, termed Hierarchical Community-aware Graph Neural Network (HC-GNN), based on hierarchical communities detected from the graph. Experiments conducted on eight datasets under transductive, inductive, and few-shot settings exhibit that HC-GNN can outperform state-of-the-art GNN models in network analysis tasks, including node classification, link prediction, and community detection