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dynamic graph embedding via Meta-Learning

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第7期36卷 2967-2979页

作者： Mao, Yuren Hao, Yu Cao, Xin Fang, Yixiang Lin, Xuemin Mao, Hua Xu, Zhiqiang Zhejiang Univ Sch Software Technol Hangzhou 310027 Zhejiang Peoples R China Enmotech Co Ltd Sydney NSW 2162 Australia Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200240 Peoples R China Univ Northumbria Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST England MBZUAI Dept Machine Learning Abu Dhabi U Arab Emirates

graphs in real-world applications usually evolve constantly presenting dynamic behaviors such as social networks and transportation networks. Hence, dynamic graph embedding has gained much attention recently. In dynamic graphs, both the topology and node attributes could change over time, which pose great challenges for developing effective embedding models. Typically, the evolution process of a dynamic graph can be recorded as a series of snapshots. We observe that the evolution process inherently provides both prior information (previous snapshots) and validation information (the next snapshot). The prior information can be used to fit the evolution process, while the validation information can be used to improve the generalization ability of a graph embedding model. However, existing dynamic graph embedding models only utilize the prior information, but overlook the validation information. To tackle this issue, this paper proposes a novel dynamic graph embedding method via Model-Agnostic Meta-Learning, which utilizes both kinds of information to obtain better graph representation. The extensive experiments on eight real-world datasets demonstrate the superiority of our proposed method over state-of-the-art methods on various graph analysis tasks.

关键词： Task analysis Metalearning Heuristic algorithms Adaptation models Multitasking Topology Data models dynamic graph embedding meta learning validation information

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DGERCL: A dynamic graph embedding Approach for Root Cause Localization in Microservice Systems

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第6期17卷 3417-3428页

作者： Cheng, Han Li, Qian Liu, Bingchen Liu, Shijun Pan, Li Shandong Univ Sch Software Jinan 250101 Peoples R China Curtin Univ Sch Elect Engn Comp & Math Sci Bentley WA 6102 Australia

Root cause localization in microservice systems refers to finding the root cause that causes system anomalies using system information. Many methods construct a graph structure and perform random walk on it to localize the root cause. This is not suitable for larger systems due to the high computational overhead. Besides, the constructed graph is usually static which mismatches with evolving metrics. Different metrics also contribute differently to determining root cause. To address these challenges, we have developed DGERCL, a novel method that employs dynamic graph embedding to localize root causes in microservice systems. We construct a dynamic graph where nodes, edges, and features correspond to microservices, invocations, and metrics. DGERCL first gets invocation information by aggregating node embedding and features via a trainable structure. An LSTM then processes invocation information to update node embedding. We also propose a neighbor information aggregation method to enrich structure information and a self-attention-inspired mechanism to leverage the importance of metrics for better mining metrics information. Finally, a classifier maps node embedding learned by LSTM to possibilities belonging to root cause. We conduct comprehensive experiments on two microservice benchmarks. Our model achieves good results which demonstrates the effectiveness of DGERCL.

关键词： Measurement Microservice architectures Location awareness Topology Long short term memory Computer architecture Vectors Root cause localization root cause analysis microservice system dynamic graph embedding

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Time-Capturing dynamic graph embedding for Temporal Linkage Evolution

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第1期35卷 958-971页

作者： Yang, Yu Cao, Jiannong Stojmenovic, Milos Wang, Senzhang Cheng, Yiran Lum, Chun Li, Zhetao Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Singidunum Univ Beograd 160622 Serbia Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Xiangtan Univ Key Lab Hunan Prov Internet Things & Informat Secu Hunan Int Sci & Technol Cooperat Base Intelligent Xiangtan 411105 Peoples R China Xiangtan Univ Coll Comp Sci Xiangtan 411105 Peoples R China

dynamic graph embedding learns representation vectors for vertices and edges in a graph that evolves over time. We aim to capture and embed the evolution of vertices' temporal connectivity. Existing work studies the vertices' dynamic connection changes but neglects the time it takes for edges to evolve, failing to embed temporal linkage information into the evolution of the graph. To capture vertices' temporal linkage evolution, we model dynamic graphs as a sequence of snapshot graphs, appending the respective timespans of edges (ToE). We co-train a linear regressor to embed ToE while inferring a common latent space for all snapshot graphs by a matrix-factorization-based model to embed vertices' dynamic connection changes. Vertices' temporal linkage evolution is captured as their moving trajectories within the common latent representation space. Our embedding algorithm converges quickly with our proposed training methods, which is very time efficient and scalable. Extensive evaluations on several datasets show that our model can achieve significant performance improvements, i.e., 22.98 percent on average across all datasets, over the state-of-the-art baselines in the tasks of vertex classification, static and time-aware link prediction, and ToE prediction.

关键词： dynamic graph embedding graph evolution edge timespan graph mining

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Toward Structure Fairness in dynamic graph embedding: A Trend-aware Dual Debiasing Approach 24

Toward Structure Fairness in Dynamic Graph Embedding: A Tren...

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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Li, Yicong Yang, Yu Cao, Jiannong Liu, Shuaiqi Tang, Haoran Xu, Guandong Educ Univ Hong Kong Hong Kong Peoples R China Univ Technol Sydney Sydney Australia Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9798400704901

Recent studies successfully learned static graph embeddings that are structurally fair by preventing the effectiveness disparity of high- and low-degree vertex groups in downstream graph mining tasks. However, achieving structure fairness in dynamic graph embedding remains an open problem. Neglecting degree changes in dynamic graphs will significantly impair embedding effectiveness without notably improving structure fairness. This is because the embedding performance of high-degree and low-to-high-degree vertices will significantly drop close to the generally poorer embedding performance of most slightly changed vertices in the longtail part of the power-law distribution. We first identify biased structural evolutions in a dynamic graph based on the evolving trend of vertex degree and then propose FairDGE, the first structurally Fair dynamic graph embedding algorithm. FairDGE learns biased structural evolutions by jointly embedding the connection changes among vertices and the long-short-term evolutionary trend of vertex degrees. Furthermore, a novel dual debiasing approach is devised to encode fair embeddings contrastively, customizing debiasing strategies for different biased structural evolutions. This innovative debiasing strategy breaks the effectiveness bottleneck of embeddings without notable fairness loss. Extensive experiments demonstrate that FairDGE achieves simultaneous improvement in the effectiveness and fairness of embeddings.

关键词： dynamic graph embedding Structural fairness Degree fairness Debiased learning Structural evolution

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Temporal Edge-Aware Hypergraph Convolutional Network for dynamic graph embedding 19th

Temporal Edge-Aware Hypergraph Convolutional Network for Dyn...

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19th Pacific Rim International Conference on Artificial Intelligence (PRICAI)

作者： Huang, Da Lei, Fangyuan Guangdong Polytech Normal Univ Sch Elect & Informat Guangzhou 510665 Peoples R China Guangdong Prov Key Lab Intellectual Property Big Guangzhou 510665 Peoples R China

ISBN: (纸本)9783031208614;9783031208621

graph embedding is a critical aspect of network analysis that helps to advance various real-world applications such as social recommendation and protein structure prediction. Most of the existing graph embedding methods are designed for static graphs while many real-world graphs intrinsically behave as dynamic graphs. Recent works try to combine graph neural networks(GNN) with recurrent neural networks to address this issue. However, these methods can not independently utilize GNN models to cope with dynamic graphs and they ignore the inner edge-level correlations in dynamic graphs. To tackle these problems, we propose a novel dynamic graph embedding framework in this paper, called DynHyper. Specifically, we introduce a temporal hypergraph construction to capture the local structure information and temporal dynamics simultaneously. Then, we employ a hyperedge projection to obtain edge-level correlations. Further, we propose a temporal edge-aware hypergraph convolution to transmit and aggregate the messages in the temporal hypergraph. We conduct our experiments on seven real-world datasets to evaluate the effectiveness of DynHyper in both link prediction and node classification tasks. Experimental results show that DynHyper significantly outperforms all baselines, especially on the more complex datasets.

关键词： graph convolutional networks dynamic graph embedding Hypergraph learning

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The influence of residue interaction on thermal stability of lipase based on dynamic graph embedding

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FOOD BIOSCIENCE 2024年 62卷

作者： Cai, Yupeng Zou, Xiaoyang Ding, Yanrui Jiangnan Univ Sch Sci Wuxi 214122 Jiangsu Peoples R China

Lipases with high thermal stability have greater application in industrial production. Focusing on wild-type lipase and their thermotolerant mutants, this study employs dynamic graph embedding to explore the change of spatiotemporal characteristics in residue-residue interactions, analyzing the relationship between residue mutations and the thermal stability of lipase. K-means clustering is used to analyze the synergistic relationships between residues, revealing that thermotolerant mutants achieve higher thermal stability by enhancing the synergistic interactions between secondary structures, resulting in a more balanced and tightly-knit internal structure. Critical residues are identified using anomaly node detection methods, showing that residues with spatiotemporal instability are mainly distributed on highly flexible loop regions. Specifically, mutations at Tyr89 and Asp132 restrict the motion range of the loops, making the lipase structure more stable. The study demonstrates that mutating highly flexible regions to increase structural rigidity can effectively enhance the thermal stability of lipases.

关键词： Lipase Thermal stability dynamic graph embedding Residue synergy Critical residue

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Sludge bulking monitoring in industrial wastewater treatment plants through graphical methods: A dynamic graph embedding and Bayesian networks approach

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023年第1期345卷 118804页

作者： Loy-Benitez, Jorge Tariq, Shahzeb Nguyen, Hai Tra Heo, SungKu Yoo, ChangKyoo Kyung Hee Univ Coll Engn Dept Environm Sci & Engn Integrated Engn 1732 Deogyeong Daero Yongin 17104 Gyeonggi Do South Korea Hanyang Univ Dept Earth Resources & Environm Engn 222 Wangsimni Ro Seoul 04763 South Korea

Sludge bulking is a prevalent issue in wastewater treatment plants (WWTPs) that negatively impacts effluent quality by hindering the normal functioning of treatment processes. To tackle this problem, we propose a novel graph-based monitoring framework that employs advanced graph-based techniques to detect and diagnose sludge bulking events. The proposed framework utilizes historical datasets under normal operating conditions to extract pertinent features and causal relationships between process variables. This enables operators to trigger alarms and diagnose the root cause of the bulking event. Sludge bulking detection is carried out using the dynamic graph embedding (DGE) method, which identifies similarities among process variables in both temporal and neighborhood dependencies. Consequently, the dynamic Bayesian network (DBN) computes the prior and posterior probabilities of a belief, updated at each time step. Variations in these probabilities indicate the potential root cause of the sludge bulking event. The results demonstrate that the DGE outperforms other linear and non-linear feature extraction methods, achieving a detection rate of 99%, zero false alarms, and less than one percent incorrect detections. Additionally, the DBN-based diagnostic method accurately identified the majority of sludge bulking root causes, primarily those resulting from sudden drops in COD concentration, with an accuracy of 98% an improvement of 11% over state-of-the-art techniques.

关键词： dynamic graph embedding dynamic Bayesian networks graph-based monitoring Sludge bulking Wastewater treatment

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Seizure detection from multi-channel EEG using entropy-based dynamic graph embedding

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2021年 122卷 102201-102201页

作者： Li, Gen Jung, Jason J. Chung Ang Univ Dept Comp Engn 84 Heukseok Ro Seoul South Korea

An epileptic seizure is a chronic disease with sudden abnormal discharge of brain neurons, which leads to transient brain dysfunction. To detect epileptic seizures, we propose a novel idea based on a dynamic graph embedding model. The dynamic graph is built by identifying the correlation among the multi-channel EEG signals. graph entropy measurement is exploited to calculate the similarity among the graph at each time interval and construct the graph embedding space. Since the abnormal electrical brain activity causes the epileptic seizure, the graph entropy during the seizure time interval is different from other time intervals. Therefore, we propose an entropy-based dynamic graph embedding model to cluster the graphs, and the graphs with epileptic seizures are discriminated. We applied the proposed approach to the Children Hospital Boston-Massachusetts Institute of Technology Scalp EEG database. The results have shown that the proposed approach outperformed the baselines by 1.4% with respect to accuracy.

关键词： Seizure detection dynamic graph embedding graph entropy

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FeatureNorm: L2 Feature Normalization for dynamic graph embedding 20

FeatureNorm: L2 Feature Normalization for Dynamic Graph Embe...

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20th IEEE International Conference on Data Mining (ICDM)

作者： Yang, Menglin Meng, Ziqiao King, Irwin Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

ISBN: (纸本)9781728183169

dynamic graphs arise in a plethora of practical scenarios such as social networks, communication networks, and financial transaction networks. Given a dynamic graph, it is fundamental and essential to learn a graph representation that is expected not only to preserve structural proximity but also jointly capture the time-evolving patterns. Recently, graph convolutional network (GCN) has been widely explored and used in non-Euclidean application domains. The main success of GCN, especially in handling dependencies and passing messages within nodes, lies in its approximation to Laplacian smoothing. As a matter of fact, this smoothing technique can not only encourage must-link node pairs to get closer but also push cannot-link pairs to shrink together, which potentially cause serious feature shrink or oversmoothing problem, especially when stacking graph convolution in multiple layers or steps. For learning time-evolving patterns, a natural solution is to preserve historical state and combine it with the current interactions to obtain the most recent representation. Then the serious feature shrink or oversmoothing problem could happen when stacking graph convolution explicitly or implicitly according to current prevalent methods, which would make nodes too similar to distinguish each other. To solve this problem in dynamic graph embedding, we analyze the shrinking properties in the node embedding space at first, and then design a simple yet versatile method, which exploits L2 feature normalization constraint to rescale all nodes to hypersphere of a unit ball so that nodes would not shrink together, and yet similar nodes can still get closer. Extensive experiments on four real-world dynamic graph datasets compared with competitive baseline models demonstrate the effectiveness of the proposed method.

关键词： dynamic graph embedding Feature shrink Normalization graph convolutional network

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DyngraphGAN: dynamic graph embedding via Generative Adversarial Networks 1

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24th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Xiong, Yun Zhang, Yao Fu, Hanjie Wang, Wei Zhu, Yangyong Yu, Philip S. Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai Peoples R China Fudan Univ Shanghai Inst Adv Commun & Data Sci Shanghai Peoples R China Univ Calif Los Angeles Los Angeles CA USA Univ Illinois Comp Sci Dept Chicago IL USA

ISBN: (数字)9783030185763

ISBN: (纸本)9783030185763;9783030185756

graphs have become widely adopted as a means of representing relationships between entities in many applications. These graphs often evolve over time. Learning effective representations preserving graph topology, as well as latent patterns in temporal dynamics, has drawn increasing interests. In this paper, we investigate the problem of dynamic graph embedding that maps a time series of graphs to a low dimensional feature space. However, most existing works in the field of dynamic representation learning either consider temporal evolution of low-order proximity or treat high-order proximity and temporal dynamics separately. It is challenging to learn one single embedding that can preserve the high-order proximity with long-term temporal dependencies. We propose a Generative Adversarial Networks (GAN) based model, named DyngraphGAN, to learn robust feature representations. It consists of a generator and a discriminator trained in an adversarial process. The generator generates connections between nodes that are represented by a series of adjacency matrices. The discriminator integrates a graph convolutional network for high-order proximity and a convolutional neural network for temporal dependency to distinguish real samples from fake samples produced by the generator. With iterative boosting of the performance of the generator and discriminator, node embeddings are learned to present dynamic evolution over time. By jointly considering high-order proximity and temporal evolution, our model can preserve spatial structure with temporal dependency. DyngraphGAN is optimized on subgraphs produced by random walks to capture more complex structural and temporal patterns in the dynamic graphs. We also leverage sparsity and temporal smoothness properties to further improve the model efficiency. Our model demonstrates substantial gains over several baseline models in link prediction and reconstruction tasks on real-world datasets.

关键词： dynamic graph embedding Adversarial Network Data mining Representation learning

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