HEA-D: A Hybrid Evolutionary Algorithm for Diversified Top-k Weight Clique Search Problem

Cui, Zijun; Yin, Naiyu; Wang, Yuru; Ji, Qiang

doi:10.24963/ijcai.2022/668

Cited by 2 publications

(2 citation statements)

References 4 publications

(4 reference statements)

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“…Graphs are an important data structure [32][33][34][35], and GNNs are a type of method for solving graph problems that has attracted much attention recently [36,37]. Their applications span a wide range of fields, including federated learning [38], information security [39][40][41], anomaly detection [42][43][44], and the financial sector [45].…”

Section: Graph Neural Network For Session-based Recommendationmentioning

confidence: 99%

A Time-Sensitive Graph Neural Network for Session-Based New Item Recommendation

Wang,

Jin

2024

Electronics

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Session-based recommendation plays an important role in daily life and exists in many scenarios, such as online shopping websites and streaming media platforms. Recently, some works have focused on using graph neural networks (GNNs) to recommend new items in session-based scenarios. However, these methods have encountered several limitations. First, existing methods typically ignore the impact of items’ visited time in constructing session graphs, resulting in a departure from real-world recommendation dynamics. Second, sessions are often sparse, making it challenging for GNNs to learn valuable item embedding and user preferences. Third, the existing methods usually overemphasize the impact of the last item on user preferences, neglecting their interest in multiple items in a session. To address these issues, we introduce a time-sensitive graph neural network for new item recommendation in session-based scenarios, namely, TSGNN. Specifically, TSGNN provides a novel time-sensitive session graph constructing technique to solve the first problem. For the second problem, TSGNN introduces graph augmentation and contrastive learning into it. To solve the third problem, TSGNN designs a time-aware attention mechanism to accurately discern user preferences. By evaluating the compatibility between user preferences and candidate new item embeddings, our method recommends items with high relevance scores for users. Comparative experiments demonstrate the superiority of TSGNN over state-of-the-art (SOTA) methods.

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Section: Graph Neural Network For Session-based Recommendationmentioning

confidence: 99%

A Time-Sensitive Graph Neural Network for Session-Based New Item Recommendation

Wang,

Jin

2024

Electronics

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“…With graph-related problems rising in various real-world scenarios (Wu et al 2022;Zheng et al 2023b;Wu et al 2023a), GNNs have emerged as a powerful paradigm for tackling complex graph data (Liu et al 2023c;Zheng et al 2023dZheng et al , 2022. GNNs have shown remarkable success across various domains (Zhang et al 2022), including social networks (Zheng et al 2022), anomaly detection (Liu et al 2023b), binary code analysis (Jin et al 2022), and recommender systems (Jin et al 2023b,a).…”

Section: Related Work Graph Neural Networkmentioning

confidence: 99%

GOODAT: Towards Test-Time Graph Out-of-Distribution Detection

Wang,

He,

Zhang

et al. 2024

AAAI

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Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. While GNNs excel in scenarios where the testing data shares the distribution of their training counterparts (in distribution, ID), they often exhibit incorrect predictions when confronted with samples from an unfamiliar distribution (out-of-distribution, OOD). To identify and reject OOD samples with GNNs, recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN. Despite their effectiveness, these methods come with heavy training resources and costs, as they need to optimize the GNN-based models on training data. Moreover, their reliance on modifying the original GNNs and accessing training data further restricts their universality. To this end, this paper introduces a method to detect Graph Out-of-Distribution At Test-time (namely GOODAT), a data-centric, unsupervised, and plug-and-play solution that operates independently of training data and modifications of GNN architecture. With a lightweight graph masker, GOODAT can learn informative subgraphs from test samples, enabling the capture of distinct graph patterns between OOD and ID samples. To optimize the graph masker, we meticulously design three unsupervised objective functions based on the graph information bottleneck principle, motivating the masker to capture compact yet informative subgraphs for OOD detection. Comprehensive evaluations confirm that our GOODAT method outperforms state-of-the-art benchmarks across a variety of real-world datasets.

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HEA-D: A Hybrid Evolutionary Algorithm for Diversified Top-k Weight Clique Search Problem

Cited by 2 publications

References 4 publications

A Time-Sensitive Graph Neural Network for Session-Based New Item Recommendation

A Time-Sensitive Graph Neural Network for Session-Based New Item Recommendation

GOODAT: Towards Test-Time Graph Out-of-Distribution Detection

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