Deep Semi-supervised Anomaly Detection with Metapath-based Context Knowledge

Graph anomaly detection has attracted considerable attention in recent years. This paper introduces a novel approach that leverages metapath-based semi-supervised learning, addressing the limitations of previous methods. We present a new framework, Metapath-based Semi-supervised Anomaly Detection (MSAD), incorporating GCN layers in both the encoder and decoder to efficiently propagate context information between abnormal and normal nodes. The design of metapath-based context information and a specifically crafted anomaly community enhance the process of learning differences in structures and attributes, both globally and locally. Through a comprehensive set of experiments conducted on seven real-world networks, this paper demonstrates the superiority of the MSAD method compared to state-of-the-art techniques. The promising results of this study pave the way for future investigations, focusing on the optimization and analysis of metapath patterns to further enhance the effectiveness of anomaly detection on attributed networks

Exploring Cohesive Subgraphs in Hypergraphs: The (k,g)-core Approach

Identifying cohesive subgraphs in hypergraphs is a fundamental problem that has received recent attention in data mining and engineering fields. Existing approaches mainly focus on a strongly induced subhypergraph or edge cardinality, overlooking the importance of the frequency of co-occurrence. In this paper, we propose a new cohesive subgraph named (k,g)-core, which considers both neighbour and co-occurrence simultaneously. The $(k,g)$-core has various applications including recommendation system, network analysis, and fraud detection. To the best of our knowledge, this is the first work to combine these factors. We extend an existing efficient algorithm to find solutions for $(k,g)$-core. Finally, we conduct extensive experimental studies that demonstrate the efficiency and effectiveness of our proposed algorithm.Comment: 5 page

Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges

Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems. Graph anomalies are patterns in a graph that do not conform to normal patterns expected of the attributes and/or structures of the graph. In recent years, graph neural networks (GNNs) have been studied extensively and have successfully performed difficult machine learning tasks in node classification, link prediction, and graph classification thanks to the highly expressive capability via message passing in effectively learning graph representations. To solve the graph anomaly detection problem, GNN-based methods leverage information about the graph attributes (or features) and/or structures to learn to score anomalies appropriately. In this survey, we review the recent advances made in detecting graph anomalies using GNN models. Specifically, we summarize GNN-based methods according to the graph type (i.e., static and dynamic), the anomaly type (i.e., node, edge, subgraph, and whole graph), and the network architecture (e.g., graph autoencoder, graph convolutional network). To the best of our knowledge, this survey is the first comprehensive review of graph anomaly detection methods based on GNNs.Comment: 9 pages, 2 figures, 1 tables; to appear in the IEEE Access (Please cite our journal version.

SSumM: Sparse Summarization of Massive Graphs

Given a graph G and the desired size k in bits, how can we summarize G within k bits, while minimizing the information loss? Large-scale graphs have become omnipresent, posing considerable computational challenges. Analyzing such large graphs can be fast and easy if they are compressed sufficiently to fit in main memory or even cache. Graph summarization, which yields a coarse-grained summary graph with merged nodes, stands out with several advantages among graph compression techniques. Thus, a number of algorithms have been developed for obtaining a concise summary graph with little information loss or equivalently small reconstruction error. However, the existing methods focus solely on reducing the number of nodes, and they often yield dense summary graphs, failing to achieve better compression rates. Moreover, due to their limited scalability, they can be applied only to moderate-size graphs. In this work, we propose SSumM, a scalable and effective graph-summarization algorithm that yields a sparse summary graph. SSumM not only merges nodes together but also sparsifies the summary graph, and the two strategies are carefully balanced based on the minimum description length principle. Compared with state-of-the-art competitors, SSumM is (a) Concise: yields up to 11.2X smaller summary graphs with similar reconstruction error, (b) Accurate: achieves up to 4.2X smaller reconstruction error with similarly concise outputs, and (c) Scalable: summarizes 26X larger graphs while exhibiting linear scalability. We validate these advantages through extensive experiments on 10 real-world graphs.Comment: to be published in the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '20

A Unified Compression Framework for Efficient Speech-Driven Talking-Face Generation

Virtual humans have gained considerable attention in numerous industries, e.g., entertainment and e-commerce. As a core technology, synthesizing photorealistic face frames from target speech and facial identity has been actively studied with generative adversarial networks. Despite remarkable results of modern talking-face generation models, they often entail high computational burdens, which limit their efficient deployment. This study aims to develop a lightweight model for speech-driven talking-face synthesis. We build a compact generator by removing the residual blocks and reducing the channel width from Wav2Lip, a popular talking-face generator. We also present a knowledge distillation scheme to stably yet effectively train the small-capacity generator without adversarial learning. We reduce the number of parameters and MACs by 28$\times$ while retaining the performance of the original model. Moreover, to alleviate a severe performance drop when converting the whole generator to INT8 precision, we adopt a selective quantization method that uses FP16 for the quantization-sensitive layers and INT8 for the other layers. Using this mixed precision, we achieve up to a 19$\times$ speedup on edge GPUs without noticeably compromising the generation quality.Comment: MLSys Workshop on On-Device Intelligence, 2023; Demo: https://huggingface.co/spaces/nota-ai/compressed_wav2li

Microvasculature remodeling in the mouse lower gut during inflammaging

Inflammaging is defined as low-grade, chronic, systemic inflammation in aging, in the absence of overt infection. Age-associated deterioration of gastrointestinal function could be ascribed to the inflammaging, although evidence is yet to emerge. Here we show that microvessels in aging mouse intestine were progressively deprived of supportive structures, microvessel-associated pericytes and adherens junction protein vascular endothelial (VE)-cadherin, and became leaky. This alteration was ascribed to up-regulation of angiopoetin-2 in microvascular endothelial cells. Up-regulation of the angiopoietin-2 was by TNF-α, originated from M2-like residential CD206 + macrophages, proportion of which increases as animal ages. It was concluded that antigenic burdens encountered in intestine throughout life create the condition of chronic stage of inflammation, which accumulates M2-like macrophages expressing TNF-α. The TNF-α induces vascular leakage to facilitate recruitment of immune cells into intestine under the chronic inflammatory setting. © Author(s) 2017.1

The Fate of Tau Aggregates Between Clearance and Transmission

Neuronal accumulation of mis-folded tau is the pathological hallmark of multiple neurodegenerative disorders, including Alzheimer’s disease. Distinct from amyloid plaques, which appear simultaneously throughout the brain, tau pathology develops first in a specific brain region and then propagates to neuroanatomically connected brain regions, exacerbating the disease. Due to the implication in disease progression, prevention of tau transmission is recognized as an important therapeutic strategy that can halt disease progression in the brain. Recently, accumulating studies have demonstrated diverse cellular mechanisms associated with cell-to-cell transmission of tau. Once transmitted, mis-folded tau species act as a prion-like seed for native tau aggregation in the recipient neuron. In this review, we summarize the diverse cellular mechanisms associated with the secretion and uptake of tau, and highlight tau-trafficking receptors, which mediate tau clearance or cell-to-cell tau transmission