GraphGPT: Graph Instruction Tuning for Large Language Models

Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increasing attention from both academia and industry. Many deep learning and graph learning models have been developed for effective anomaly detection in multivariate time series data, which enable advanced applications such as smart surveillance and risk management with unprecedented capabilities. Nevertheless, MTAD is facing critical challenges deriving from the dependencies among sensors and variables, which often change over time. To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring dynamic variable relationships. We combine adaptive graph learning methods with graph attention to generate a global-local graph that can represent both global correlations and dynamic local correlations among sensors. To capture inter-sensor relationships and temporal dependencies, a convolutional neural network based on the global-local graph is integrated with a temporal self-attention module to construct a coupled attention module. In addition, we develop a multilevel encoder-decoder architecture that accommodates reconstruction and prediction tasks to better characterize multivariate time series data. Extensive experiments on real-world datasets have been conducted to evaluate the performance of the proposed CAN approach, and the results show that CAN significantly outperforms state-of-the-art baselines.

pdf/coupled-attention-networks-for-multivariate-time-series-1fyux8tt.pdf

Coupled Attention Networks for Multivariate Time Series Anomaly Detection

Smart cities have seen a growing interest among governments, researchers, and industries. Smart cities use digital technologies to enhance the quality of life for residents while promoting sustainability and efficient resource management. By integrating various technologies such as Information and Communication Technologies (ICT), Artificial Intelligence (AI), and Internet of Things (IoT), smart cities can improve the delivery of public services, optimize transportation systems, reduce energy consumption, and enhance public safety, among other benefits. Smart cities focus on automating different disciplines, including smart environment, smart home, smart economy, smart mobility, and smart governance. As a result, multi-agent driven smart cities have received tremendous attention from the research community for obtaining intelligent solutions to complex problems in different disciplines by subdividing responsibilities into multiple agents and empowering agents through AI. In this regard, it is vital to explore the usage of multi-agent systems in different critical application areas of smart cities. In this paper, a detailed description of the multi-agent process for smart city application areas is provided, along with resources and future research directions. Four different application areas: smart home, smart governance, smart environment, and smart mobility are discussed in detail. 

Next Generation of Multi-Agent Driven Smart City Applications and Research Paradigms

Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world, particularly with respect to data privacy. Existing approaches predominantly focus on Euclidean data, such as images and videos, while the exploration of non-Euclidean graph data remains scarce. Recent graph neural network (GNN) approaches could suffer from serious performance decline due to domain shift and label scarcity in source-free adaptation scenarios. In this study, we propose a novel method named Graph Diffusion-based Alignment with Jigsaw (GALA) tailored for source-free graph domain adaptation. To achieve domain alignment, GALA employs a graph diffusion model to reconstruct source-style graphs from target data. Specifically, a score-based graph diffusion model is trained using source graphs to learn the generative source styles. Then, we introduce perturbations to target graphs via a stochastic differential equation instead of sampling from a prior, followed by the reverse process to reconstruct source-style graphs. We feed them into an off-the-shelf GNN and introduce class-specific thresholds with curriculum learning, which can generate accurate and unbiased pseudo-labels for target graphs. Moreover, we develop a simple yet effective graph mixing strategy named graph jigsaw to combine confident graphs and unconfident graphs, which can enhance generalization capabilities and robustness via consistency learning. Extensive experiments on benchmark datasets validate the effectiveness of GALA. The source code is available at https://github.com/luo-junyu/GALA. 

GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation

Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to privacy and storage concerns. To this end, we investigate an underexplored yet practical problem of source-free graph domain adaptation, which transfers knowledge from source models instead of source graphs to a target domain. To solve this problem, we introduce a novel GNN-based approach called Rank and Align (RNA), which ranks graph similarities with spectral seriation for robust semantics learning, and aligns inharmonic graphs with harmonic graphs which close to the source domain for subgraph extraction. In particular, to overcome label scarcity, we employ the spectral seriation algorithm to infer the robust pairwise rankings, which can guide semantic learning using a similarity learning objective. To depict distribution shifts, we utilize spectral clustering and the silhouette coefficient to detect harmonic graphs, which the source model can easily classify. To reduce potential domain discrepancy, we extract domain-invariant subgraphs from inharmonic graphs by an adversarial edge sampling process, which guides the invariant learning of GNNs. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our proposed RNA. 

pdf/rank-and-align-towards-effective-source-free-graph-domain-2fs1xw04xx.pdf

Rank and Align: Towards Effective Source-free Graph Domain Adaptation

Graph Convolutional Networks (GCNs) are popular for learning representation of graph data and have a wide range of applications in social networks, recommendation systems, etc. However, training GCN models for large networks is resource intensive and time consuming, which hinders them from real deployment. The existing GCN training methods intended to optimize the sampling of mini-batches for stochastic gradient descent to accelerate training process, which did not reduce the problem size and had limited reduction in computation complexity. In this paper, we argue that a GCN can be trained with a sampled subgraph to produce approximate node representations, which inspires us a novel perspective to accelerate GCN training via network sampling. To this end, we propose a label-centric cumulative sampling (LCS) framework for training GCNs for large graphs. The proposed method constructs a subgraph cumulatively based on probabilistic sampling, and trains the GCN model iteratively to generate approximate node representations. The optimality of LCS is theoretically guaranteed to minimize the bias during node aggregation procedure in GCN training. Extensive experiments based on four real-world network datasets show that the LCS framework accelerates the training for the state-of-the-art GCN models up to 17x without causing noteworthy model accuracy drop.

Resource-Efficient Training for Large Graph Convolutional Networks with Label-Centric Cumulative Sampling

Graph Neural Networks (GNNs) have emerged as a powerful tool for modeling graph-structured data, exhibiting remarkable potential in applications such as social networks, recommendation systems, and molecular structures. However, the conventional GNNs perform node-level feature aggregation from neighbors without considering graph-label information, which leads to the misaligned embedding problem that may cause a detrimental effect on graph-level tasks such as graph classification. In this paper, we propose a novel label-attentive distillation method called LAD-GNN for graph representation learning to solve this problem. It alternatively trains a teacher model and a student GNN with a distillation-based approach. In the teacher model, a label-attentive encoder is proposed to encode the label information fusing with the node features to generate ideal embedding. In the student model, the ideal embedding is used as intermediate supervision to urge the student GNN to learn class-friendly node embedding to facilitate graph-level tasks. Generally, LAD-GNN is an enhanced GNN training approach that can be incorporated with arbitrary GNN backbone to improve performance without significant increase of computational cost. Extensive experiments with 7 GNN backbones based on 10 benchmark datasets show that LAD-GNN improves the SOTA GNNs in graph classification accuracy. The source codes of LAD-GNN are publicly available on https://github.com/XiaobinHong/LAD-GNN. 

pdf/label-attentive-distillation-for-gnn-based-graph-2oyf5y01vr.pdf

Label Attentive Distillation for GNN-Based Graph Classification

Graph stream data is widely applied to describe the relationships in networks such as social networks, computer networks and hyperlink networks. Due to the large volume and high dynamicity of graph streams, several graph sketches were proposed to summarize them for fast queries. However, the existing graph sketches suffer from low performance on graph query tasks due to hash collisions between heavy and light edges. In this paper, we propose a novel learning-based Dichotomy Graph Sketch (DGS) mechanism, which adopts two separate graph sketches, a heavy sketch and a light sketch, to store heavy edges and light edges respectively. DGS periodically obtains heavy edges and light edges in a session of a graph stream, and use them as training samples to train a deep neural network (DNN) based binary classifier. The DNN-based classifier is then utilized to decide whether the upcoming edges are heavy or not, and store them in different graph sketches accordingly. With the learnable classifier and the dichotomy graph sketches, the proposed mechanism can resolve the hashing collision problem and significantly improve the accuracy for graph query tasks. We conducted extensive experiments on three real-world graph stream datasets, which show that DGS outperforms the state-of-the-art graph sketches in a variety of graph query tasks. 

Learning-Based Dichotomy Graph Sketch for Summarizing Graph Streams with High Accuracy

The research work discussed in this thesis investigated the application of combinatorics and graph theory in the analysis of the partition function of the Ising Model. Chapter 1 gives a general introduction to the partition function of the Ising Model and the Feynman Identity in the language of graph theory. Chapter 2 describes and proves combinatorially the Feynman Identity in the special case when there is only one vertex and multiple loops. Chapter 3 digresses into the number of cycles in a directed graph, along with its application in the special case to derive the analytical expression of the number of non-periodic cycles with positive and negative signs. Chapter 4 comes back to the general case of the Feynman Identity. The Feynman Identity is applied to several special cases of the graph and a combinatorial identity is established for each case. Chapter 5 concludes the thesis by summarizing the main ideas in each chapter.

Applications of Combinatorial Analysis to the Calculation of the Partition Function of the Ising Model

Inﬂuence maximization is the problem of ﬁnding a set of seed nodes in the network that maximizes the inﬂuence spread, which has become an important topic in social network analysis. Conventional inﬂuence maximization algorithms cause “unfair” inﬂuence spread among diﬀerent groups in the population, which could lead to severe bias in public opinion dissemination and viral marketing. To address this issue, we formulate the fair inﬂuence maximization problem concerning the trade-oﬀ between inﬂuence maximization and group fairness. For the purpose of solving the fair inﬂuence maximization problem in large-scale social networks eﬃciently, we propose a novel attribute-based reverse inﬂuence sampling (ABRIS) framework. This framework intends to estimate inﬂuence in speciﬁc groups with guarantee through an attribute-based hypergraph so that we can select seed nodes strategically. Therefore, under the ABRIS framework, we design two diﬀerent node selection algorithms, ABRIS-G and ABRIS-T. ABRIS-G selects nodes in a greedy scheduling way. ABRIS-T adopts a two-phase node selection method. These algorithms run eﬃciently and achieve a good trade-oﬀ between inﬂuence maximization and group fairness. Extensive experiments on six real-world social networks show that our algorithms signiﬁcantly outperform the state-of-the-art approaches.

Fair Inﬂuence Maximization in Large-scale Social Networks Based on Attribute-aware Reverse Inﬂuence Sampling

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