This work explores areas overlapping music, graph theory, and machine learning. An embedding representation of a node, in a weighted undirected graph $\mathcal{G}$, is a representation that captures the meaning of nodes in an embedding space. In this work, 383 Bach chorales were compiled and represented as a graph. Two application cases were investigated in this paper (i) learning node embedding representation using \emph{Continuous Bag of Words (CBOW), skip-gram}, and \emph{node2vec} algorithms, and (ii) learning node labels from neighboring nodes based on a collective classification approach. The results of this exploratory study ascertains many salient features of the graph-based representation approach applicable to music applications.
University of Edinburgh（爱丁堡大学）；Stanford University（斯坦福大学）
Structural features are important features in a geometrical graph. Although there are some correlation analysis of features based on covariance, there is no relevant research on structural feature correlation analysis with graph neural networks. In this paper, we introuduce graph feature to feature (Fea2Fea) prediction pipelines in a low dimensional space to explore some preliminary results on structural feature correlation, which is based on graph neural network. The results show that there exists high correlation between some of the structural features. An irredundant feature combination wit…
With the growing use of deep learning methods, particularly graph neural networks, which encode intricate interconnectedness information, for a variety of real tasks, there is a necessity for explainability in such settings. In this paper, we demonstrate the applicability of popular explainability approaches on Graph Attention Networks (GAT) for a graph-based super-pixel image classification task. We assess the qualitative and quantitative performance of these techniques on three different datasets and describe our findings. The results shed a fresh light on the notion of explainability in GNN…
University of Science and Technology of China（中国科学技术大学）；The University of Sydney（悉尼大学）
Recent studies show that Graph Neural Networks (GNNs) are vulnerable to adversarial attack, i.e., an imperceptible structure perturbation can fool GNNs to make wrong predictions. Some researches explore specific properties of clean graphs such as the feature smoothness to defense the attack, but the analysis of it has not been well-studied. In this paper, we analyze the adversarial attack on graphs from the perspective of feature smoothness which further contributes to an efficient new adversarial defensive algorithm for GNNs. We discover that the effect of the high-order graph structure is a …
Rowan University（罗文大学）；University of Minnesota（明尼苏达大学）
The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance. However, the relationship between the architecture of a DANN and its robustness to noise and adversarial attacks is less explored. We investigate how the robustness of DANNs relates to their underlying graph architectures or structures. This study: (1) starts by exploring the design space of architectures of DANNs using graph-theoretic robustness measures; (2) transforms the graphs to DANN architectures to train/validate/test on various image classification tasks; (3) exp…
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）
Graph Convolutional Neural Networks (GCNNs) are generalizations of CNNs to graph-structured data, in which convolution is guided by the graph topology. In many cases where graphs are unavailable, existing methods manually construct graphs or learn task-driven adaptive graphs. In this paper, we propose Graph Learning Neural Networks (GLNNs), which exploit the optimization of graphs (the adjacency matrix in particular) from both data and tasks. Leveraging on spectral graph theory, we propose the objective of graph learning from a sparsity constraint, properties of a valid adjacency matrix as wel…
Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Michigan State University（密歇根州立大学）；The Hong Kong Polytechnic University（香港理工大学）；Rensselaer Polytechnic Institute（纽约理工学院）
Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Graph embedding techniques aim to automatically create a low-dimensional representation of a given graph, which captures key structural elements in the resulting embedding space. However, to date, there has been little work exploring exactly which topological structures are being learned in the embeddings process. In this paper, we investigate if graph embeddings are approximating somet…
Department of Computer Science University of Quebec at Montreal（魁北克大学蒙特利尔分校计算机科学系）；University of Quebec at Montreal（魁北克大学蒙特利尔分校）
While representation learning has yielded a great success on many graph learning tasks, there is little understanding behind the structures that are being captured by these embeddings. For example, we wonder if the topological features, such as the Triangle Count, the Degree of the node and other centrality measures are concretely encoded in the embeddings. Furthermore, we ask if the presence of these structures in the embeddings is necessary for a better performance on the downstream tasks, such as clustering and classification. To address these questions, we conduct an extensive empirical st…
Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health. To better understand the transmission and evolution of epidemics, we propose EpiGNN, a graph neural network-based model for epidemic forecasting. Specifically, we design a transmission risk encoding module to characterize local and global spatial effects of regions in epidemic processes and incorporate them into the model. Meanwhile, we develop a Region-Aware Graph Learner (RAGL) that takes transmission risk, geographical dependencies, and temporal…
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；National Computer Network Emergency Response Technical Team/Coordination Center of China（国家计算机网络应急技术处理协调中心）
Modeling of relation pattern is the core focus of previous Knowledge Graph Embedding works, which represents how one entity is related to another semantically by some explicit relation. However, there is a more natural and intuitive relevancy among entities being always ignored, which is that how one entity is close to another semantically, without the consideration of any explicit relation. We name such semantic phenomenon in knowledge graph as proximity pattern. In this work, we explore the problem of how to define and represent proximity pattern, and how it can be utilized to help knowledge…
GraphDLG: Exploring Deep Leakage from Gradients in Federated Graph Learning
C-FAIR \& School of Software Shandong University Jinan China；SKLCCSE Lab Beihang University Beijing China；Automation \& Intelligent Sensing Shanghai Jiao Tong University Shanghai China；Shandong University；Beihang University；Shanghai Jiao Tong University
Dalian University of Technology（大连理工大学）；Zhejiang University of Technology（浙江工业大学）；Federation University Australia（澳大利亚联邦大学）；Deakin University（迪肯大学）
Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To reduce this research gap and learn human mobility k…
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of the Chinese Academy of Sciences（中国科学院大学）；Dalian University of Technology（大连理工大学）；Fordham University（福特汉姆大学）；Georgia Institute of Technology（佐治亚理工学院）
``Classical'' word embeddings, such as Word2Vec, have been shown to capture the semantics of words based on their distributional properties. However, their ability to represent the different meanings that a word may have is limited. Such approaches also do not explicitly encode relations between entities, as denoted by words. Embeddings of knowledge bases (KB) capture the explicit relations between entities denoted by words, but are not able to directly capture the syntagmatic properties of these words. To our knowledge, recent research have focused on representation learning that augment the …
Doctoral Program of Computer Science University of Helsinki（赫尔辛基大学计算机科学博士项目）；Research Program in Systems Oncology Faculty of Medicine University of Helsinki（赫尔辛基大学医学学院系统肿瘤研究计划）；Centre for Information Sciences and Systems Nanyang Technological University（南洋理工大学信息科学与系统中心）；University of Montreal（蒙特利尔大学）；Mila Quebec Artificial Intelligence Institute（魁北克人工智能研究所）
School of Computing, Newcastle University（纽卡斯尔大学计算机学院）；Department of Computer Science, Durham University（杜伦大学计算机科学系）
With the growing significance of graphs as an effective representation of data in numerous applications, efficient graph analysis using modern machine learning is receiving a growing level of attention. Deep learning approaches often operate over the entire adjacency matrix -- as the input and intermediate network layers are all designed in proportion to the size of the adjacency matrix -- leading to intensive computation and large memory requirements as the graph size increases. It is therefore desirable to identify efficient measures to reduce both run-time and memory requirements allowing f…
Exploring Hierarchical Molecular Graph Representation in Multimodal LLMs
National University of Singapore（新加坡国立大学）；Institute of High-Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；Centre for Frontier AI Research, Agency for Science, Technology and Research（新加坡科技研究局前沿人工智能研究中心）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
McGill University（麦吉尔大学）；Quebec AI Institute (Mila)（魁北克人工智能研究所（Mila））；Facebook AI Research (FAIR)（脸书人工智能研究院（FAIR））
Real-world knowledge graphs are often characterized by low-frequency relations - a challenge that has prompted an increasing interest in few-shot link prediction methods. These methods perform link prediction for a set of new relations, unseen during training, given only a few example facts of each relation at test time. In this work, we perform a systematic study on a spectrum of models derived by generalizing the current state of the art for few-shot link prediction, with the goal of probing the limits of learning in this few-shot setting. We find that a simple zero-shot baseline - which ign…
Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）
In this paper, we have explored the effects of different minibatch sampling techniques in Knowledge Graph Completion. Knowledge Graph Completion (KGC) or Link Prediction is the task of predicting missing facts in a knowledge graph. KGC models are usually trained using margin, soft-margin or cross-entropy loss function that promotes assigning a higher score or probability for true fact triplets. Minibatch gradient descent is used to optimize these loss functions for training the KGC models. But, as each minibatch consists of only a few randomly sampled triplets from a large knowledge graph, any…
Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective
The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Shenzhen International Center for Industrial and Applied Mathematics（深圳国际工业与应用数学中心）
