TL;DR
A community detection via graph attention auto-encoders and decoders with non-negative matrix factorization (GA-NMF) with self-training module in social networks achieves deep fusion and efficient utilization of graph information for community division.
Textual-edge graphs represent interactions through both connectivity and relation-specific text. Learning community structure from these graphs requires an encoder that preserves the relevance of individual relations when combining semantic and structural information. We propose Dual-Stage Attention Embedding for Commu...
Graph Neural Networks (GNNs) have demonstrated strong performance on graph-structured data through neighborhood aggregation. However, they often underperform on heterophilic graphs and suffer from over-smoothing, leading to suboptimal representations. We attribute these limitations to the lack of view-specific factoriz...
Community detection in multiplex networks seeks groups of nodes from several types of relations. Attention mechanisms can integrate these relations, but weights based on embedding compatibility do not explicitly measure the community support provided by a node’s neighborhood. We propose Community-Guided Attentive Fusio...
Click-through rate (CTR) prediction is a crucial task to estimate the probability that users will click on items in online platforms. Recently, researchers have incorporated semantics derived from large language models (LLMs) into representations of users and items in graph neural networks (GNNs) framework to facilitat...
Dynamic community detection is of great importance to understand the evolution of relationships among entities in social networks over time. However, existing methods rely on single-view graph information, ignoring the global structural and temporal information. To address this issue, this paper presents a dynamic comm...
Community Search (CS) aims to retrieve query-dependent cohesive subgraphs from networks, playing a crucial role in modern applications such as computing power networks and biological systems. Given the high cost of labeling, unsupervised CS has emerged as a scalable paradigm. However, existing approaches often suffer f...
A cross-section of the MIT community convened discussions on topics ranging from data centers and super pollutants to agriculture and education.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
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