Recent advancements in social bot detection have been driven by the adoption of Graph Neural Networks. The social graph, constructed from social network interactions, contains benign and bot accounts that influence each other. However, previous graph-based detection methods that follow the transductive message-passing paradigm may not fully utilize hidden graph information and are vulnerable to adversarial bot behavior. The indiscriminate message passing between nodes from different categories and communities results in excessively homogeneous node representations, ultimately reducing the effectiveness of social bot detectors. In this paper, we propose \SEBot, a novel multi-view graph-based contrastive learning-enabled social bot detector. In particular, we use structural entropy as an uncertainty metric to optimize the entire graph's structure and subgraph-level granularity, revealing the implicitly existing hierarchical community structure. And we design an encoder to enable message passing beyond the homophily assumption, enhancing robustness to adversarial behaviors of social bots. Finally, we employ multi-view contrastive learning to maximize mutual information between different views and enhance the detection performance through multi-task learning. Experimental results demonstrate that our approach significantly improves the performance of social bot detection compared with SOTA methods.
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Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs
PDF ↗Tabular datasets play a crucial role in various applications. Thus, developing efficient, effective, and widely compatible prediction algorithms for tabular data is important. Currently, two prominent model types, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks (DNNs), have demonstrated performance advantages on distinct tabular prediction tasks. However, selecting an effective model for a specific tabular dataset is challenging, often demanding time-consuming hyperparameter tuning. To address this model selection dilemma, this paper proposes a new framework that amalgamates the advantages of both GBDTs and DNNs, resulting in a DNN algorithm that is as efficient as GBDTs and is competitively effective regardless of dataset preferences for GBDTs or DNNs. Our idea is rooted in an observation that deep learning (DL) offers a larger parameter space that can represent a well-performing GBDT model, yet the current back-propagation optimizer struggles to efficiently discover such optimal functionality. On the other hand, during GBDT development, hard tree pruning, entropy-driven feature gate, and model ensemble have proved to be more adaptable to tabular data. By combining these key components, we present a Tree-hybrid simple MLP (T-MLP). In our framework, a tensorized, rapidly trained GBDT feature gate, a DNN architecture pruning approach, as well as a vanilla back-propagation optimizer collaboratively train a randomly initialized MLP model. Comprehensive experiments show that T-MLP is competitive with extensively tuned DNNs and GBDTs in their dominating tabular benchmarks (88 datasets) respectively, all achieved with compact model storage and significantly reduced training duration. The codes and full experiment results are available at https://github.com/jyansir/tmlp.
Federated Learning (FL) is increasingly vulnerable to model poisoning attacks, where malicious clients degrade the global model's accuracy with manipulated updates. Unfortunately, most existing defenses struggle to handle the scenarios when multiple adversaries exist, and often rely on historical or validation data, rendering them ill-suited for the dynamic and diverse nature of real-world FL environments. Exacerbating these limitations is the fact that most existing defenses also fail to account for the distinctive contributions of Deep Neural Network (DNN) layers in detecting malicious activity, leading to the unnecessary rejection of benign updates. To bridge these gaps, we introduce FedRoLa, a cutting-edge similarity-based defense method optimized for FL. Specifically, FedRoLa leverages global model parameters and client updates independently, moving away from reliance on historical or validation data. It features a unique layer-based aggregation with dynamic layer selection, enhancing threat detection, and includes a dynamic probability method for balanced security and model performance. Through comprehensive evaluations using different DNN models and real-world datasets, FedRoLa demonstrates substantial improvements over the status quo approaches in global model accuracy, achieving up to 4% enhancement in terms of accuracy, reducing false positives to 6.4%, and securing an 92.8% true positive rate.
Graph Neural Architecture Search (GNAS) has achieved superior performance on various graph-structured tasks. However, existing GNAS studies overlook the applications of GNAS in resource-constrained scenarios. This paper proposes to design a joint graph data and architecture mechanism, which identifies important sub-architectures via the valuable graph data. To search for optimal lightweight Graph Neural Networks (GNNs), we propose a Lightweight Graph Neural Architecture Search with Graph SparsIfication and Network Pruning (GASSIP) method. In particular, GASSIP comprises an operation-pruned architecture search module to enable efficient lightweight GNN search. Meanwhile, we design a novel curriculum graph data sparsification module with an architecture-aware edge-removing difficulty measurement to help select optimal sub-architectures. With the aid of two differentiable masks, we iteratively optimize these two modules to efficiently search for the optimal lightweight architecture. Extensive experiments on five benchmarks demonstrate the effectiveness of GASSIP. Particularly, our method achieves on-par or even higher node classification performance with half or fewer model parameters of searched GNNs and a sparser graph.
Heterogeneous data widely exists in various high-impact applications. Domain adaptation and out-of-distribution generalization paradigms have been formulated to handle the data heterogeneity across domains. However, most existing domain adaptation and out-of-distribution generalization algorithms do not explicitly explain how the label information can be adaptively propagated from the source domains to the target domain. Furthermore, little effort has been devoted to theoretically understanding the convergence of existing algorithms based on neural networks. To address these problems, in this paper, we propose a generic distributional network of networks (TENON) framework, where each node of the main network represents an individual domain associated with a domain-specific network. In this case, the edges within the main network indicate the domain similarity, and the edges within each network indicate the sample similarity. The crucial idea of TENON is to characterize the within-domain label smoothness and cross-domain parameter smoothness in a unified framework. The convergence and optimality of TENON are theoretically analyzed. Furthermore, we show that based on the TENON framework, domain adaptation and out-of-distribution generalization can be naturally formulated as transductive and inductive distribution learning problems, respectively. This motivates us to develop two instantiated algorithms (TENON-DA and TENON-OOD) of the proposed TENON framework for domain adaptation and out-of-distribution generalization. The effectiveness and efficiency of TENON-DA and TENON-OOD are verified both theoretically and empirically.
Modeling the evolution of physical dynamics is a foundational problem in science and engineering, and it is regarded as the modeling of an operator mapping between infinite-dimensional functional spaces. Operator learning methods, learning the underlying infinite-dimensional operator in a high-dimensional latent space, have shown significant potential in modeling physical dynamics. However, there remains insufficient research on how to approximate an infinite-dimensional operator using a finite-dimensional parameter space. Inappropriate dimensionality representation of the underlying operator leads to convergence difficulties, decreasing generalization capability, and violating the physical consistency. To address the problem, we present Neural Manifold Operator (NMO) to learn the invariant subspace with the intrinsic dimension to parameterize infinite-dimensional underlying operators. NMO achieves state-of-the-art performance in statistical and physical metrics and gains 23.35% average improvement on three real-world scenarios and four equation-governed scenarios across a wide range of multi-disciplinary fields. Our paradigm has demonstrated universal effectiveness across various model structure implementations, including Multi-Layer Perceptron, Convolutional Neural Networks, and Transformers. Experimentally, we prove that the intrinsic dimension calculated by our paradigm is the optimal dimensional representation of the underlying operators. We release our code at https://github.com/AI4EarthLab/Neural-Manifold-Operators.
In recommendation systems, new items are continuously introduced, initially lacking interaction records but gradually accumulating them over time. Accurately predicting the click-through rate (CTR) for these items is crucial for enhancing both revenue and user experience. While existing methods focus on enhancing item ID embeddings for new items within general CTR models, they tend to adopt a global feature interaction approach, often overshadowing new items with sparse data by those with abundant interactions. Addressing this, our work introduces EmerG, a novel approach that warms up cold-start CTR prediction by learning item-specific feature interaction patterns. EmerG utilizes hypernetworks to generate an item-specific feature graph based on item characteristics, which is then processed by a Graph Neural Network (GNN). This GNN is specially tailored to provably capture feature interactions at any order through a customized message passing mechanism. We further design a meta learning strategy that optimizes parameters of hypernetworks and GNN across various item CTR prediction tasks, while only adjusting a minimal set of item-specific parameters within each task. This strategy effectively reduces the risk of overfitting when dealing with limited data. Extensive experiments on benchmark datasets validate that EmerG consistently performs the best given no, a few and sufficient instances of new items.
Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement
PDF ↗Link prediction, as a fundamental task for graph neural networks (GNNs), has boasted significant progress in varied domains. Its success is typically influenced by the expressive power of node representation, but recent developments reveal the inferior performance of low-degree nodes owing to their sparse neighbor connections, known as the degree-based long-tailed problem. Will the degree-based long-tailed distribution similarly constrain the efficacy of GNNs on link prediction? Unexpectedly, our study reveals that only a mild correlation exists between node degree and predictive accuracy, and more importantly, the number of common neighbors between node pairs exhibits a strong correlation with accuracy. Considering node pairs with less common neighbors, i.e., tail node pairs, make up a substantial fraction of the dataset but achieve worse performance, we propose that link prediction also faces the long-tailed problem. Therefore, link prediction of GNNs is greatly hindered by the tail node pairs. After knowing the weakness of link prediction, a natural question is how can we eliminate the negative effects of the skewed long-tailed distribution on common neighbors so as to improve the performance of link prediction? Towards this end, we introduce our long-tailed framework (LTLP), which is designed to enhance the performance of tail node pairs on link prediction by increasing common neighbors. Two key modules in LTLP respectively supplement high-quality edges for tail node pairs and enforce representational alignment between head and tail node pairs within the same category, thereby improving the performance of tail node pairs. Empirical results across five datasets confirm that our approach not only achieves SOTA performance but also greatly reduces the performance bias between the head and tail. These findings underscore the efficacy and superiority of our framework in addressing the long-tailed problem in link prediction.
Graph neural networks (GNNs) have revolutionized molecule representation learning by modeling molecules as graphs, with atoms represented as nodes and chemical bonds as edges. Despite their progress, they struggle with out-of-distribution scenarios, such as changes in size or scaffold of molecules with identical properties. Some studies attempt to mitigate this issue through graph invariant learning, which penalizes prediction variance across environments to learn invariant representations. But in the realm of molecules, core functional groups forming privileged substructures dominate molecular properties and remain invariant across distribution shifts. This highlights the need for integrating this prior knowledge and ensuring the environment split compatible with molecule invariant learning. To bridge this gap, we propose a novel framework named MILI. Specifically, we first formalize molecule invariant learning based on privileged substructure identification and introduce substructure invariance constraint. Building on this foundation, we theoretically establish two criteria for environment splits conducive to molecule invariant learning. Inspired by these criteria, we develop a dual-head graph neural network. A shared identifier identifies privileged substructures, while environment and task heads generate predictions based on variant and privileged substructures. Through the interaction of two heads, the environments are split and optimized to meet our criteria. The unified MILI guarantees that molecule invariant learning and environment split achieve mutual enhancement from theoretical analysis and network design. Extensive experiments across eight benchmarks validate the effectiveness of MILI compared to state-of-the-art baselines.
Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based learning tasks. Notably, most current GNN architectures operate under the assumption of homophily, whether explicitly or implicitly. While this underlying assumption is frequently adopted, it is not universally applicable, which can result in potential shortcomings in learning effectiveness. In this paper, or the first time, we transfer the prevailing concept of "one node one receptive field" to the heterophilic graph. By constructing a proxy label predictor, we enable each node to possess a latent prediction distribution, which assists connected nodes in determining whether they should aggregate their associated neighbors. Ultimately, every node can have its own unique aggregation hop and pattern, much like each snowflake is unique and possesses its own characteristics. Based on observations, we innovatively introduce the Heterophily Snowflake Hypothesis and provide an effective solution to guide and facilitate research on heterophilic graphs and beyond. We conduct comprehensive experiments including (1) main results on 10 graphs with varying heterophily ratios across 10 backbones; (2) scalability on various deep GNN backbones (SGC, JKNet, etc.) across various large number of layers (2,4,6,8,16,32 layers); (3) comparison with conventional snowflake hypothesis; (4) efficiency comparison with existing graph pruning algorithms. Our observations show that our framework acts as a versatile operator for diverse tasks. It can be integrated into various GNN frameworks, boosting performance in-depth and offering an explainable approach to choosing the optimal network depth. The source code is available at https://github.com/bingreeky/HeteroSnoH.
Prior research on neural architecture search (NAS) for adversarial robustness has revealed that a lightweight and adversarially robust sub-network could exist in a non-robust large teacher network. Such a sub-network is generally discovered based on heuristic rules to perform neural architecture search. However, heuristic rules are inadequate to handle diverse adversarial attacks and different "teacher" network capacity. To address this key challenge, we propose Reinforced Compressive Neural Architecture Search (RC-NAS), aiming to achieve Versatile Adversarial Robustness. Specifically, we define novel task settings that compose datasets, adversarial attacks, and teacher network configuration. Given diverse tasks, we develop an innovative dual-level training paradigm that consists of a meta-training and a fine-tuning phase to effectively expose the RL agent to diverse attack scenarios (in meta-training), and make it adapt quickly to locate an optimal sub-network (in fine-tuning) for previously unseen scenarios. Experiments show that our framework could achieve adaptive compression towards different initial teacher networks, datasets, and adversarial attacks, resulting in more lightweight and adversarially robust architectures. We also provide a theoretical analysis to explain why the reinforcement learning (RL)-guided adversarial architectural search helps adversarial robustness over standard adversarial training methods.
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
PDF ↗Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many domains, it is highly desirable to derive data-driven global explanations or rules that can better explain the high-level properties of the models and data in question. However, evaluating global counterfactual explanations is hard in real-world datasets due to a lack of human-annotated ground truth, which limits their use in areas like molecular sciences. Additionally, the increasing scale of these datasets provides a challenge for random search-based methods. In this paper, we develop a novel global explanation model RLHEX for molecular property prediction. It aligns the counterfactual explanations with human-defined principles, making the explanations more interpretable and easy for experts to evaluate. RLHEX includes a VAE-based graph generator to generate global explanations and an adapter to adjust the latent representation space to human-defined principles. Optimized by Proximal Policy Optimization (PPO), the global explanations produced by RLHEX cover 4.12% more input graphs and reduce the distance between the counterfactual explanation set and the input set by 0.47% on average across three molecular datasets. RLHEX provides a flexible framework to incorporate different human-designed principles into the counterfactual explanation generation process, aligning these explanations with domain expertise. The code and data are released at https://github.com/dqwang122/RLHEX.
The graph classification problem has been widely studied; however, achieving an interpretable model with high predictive performance remains a challenging issue. This paper proposes an interpretable classification algorithm for attributed graph data, called LAGRA (Learning Attributed GRAphlets). LAGRA learns importance weights for small attributed subgraphs, called attributed graphlets (AGs), while simultaneously optimizing their attribute vectors. This enables us to obtain a combination of subgraph structures and their attribute vectors that strongly contribute to discriminating different classes. A significant characteristics of LAGRA is that all the subgraph structures in the training dataset can be considered as a candidate structures of AGs. This approach can explore all the potentially important subgraphs exhaustively, but obviously, a naïve implementation can require a large amount of computations. To mitigate this issue, we propose an efficient pruning strategy by combining the proximal gradient descent and a graph mining tree search. Our pruning strategy can ensure that the quality of the solution is maintained compared to the result without pruning. We empirically demonstrate that LAGRA has superior or comparable prediction performance to the standard existing algorithms including graph neural networks, while using only a small number of AGs in an interpretable manner.
Traditional Neural Processes (NPs) and their variants aim to learn relationships between context sample points but do not consider multi-level information, resulting in a limited ability to learn complex distributions.This paper draws inspiration from features such as the hierarchical nature and interpretability of tree-like structures. This paper proposes a Hierarchical Linear Symbolized Tree-structured Neural Processes (HLNPs) architecture. This framework utilizes variables to build a top-down hierarchical linear symbolized tree-structured network architecture, enhancing positional representation information in a hierarchical manner along the deterministic path. In the latent distribution, the hierarchical linear symbolized tree-structured network approximates functions discretely through a layered approach. By decomposing the latent complex distribution into several simpler sub-problems using sum and product symbols, the upper bound of optimization is thereby increased. The tree structure discretizes variables to capture model uncertainty in the form of entropy. This approach also imparts a causal effect to the HLNPs model. Finally, we demonstrate the effectiveness of the HLNPs models for 1D data, Bayesian optimization, and 2D data.
Memory-based Temporal Graph Neural Networks (MTGNNs) are a class of temporal graph neural networks that utilize a node memory module to capture and retain long-term temporal dependencies, leading to superior performance compared to memory-less counterparts. However, the iterative reading and updating process of the memory module in MTGNNs to obtain up-to-date information needs to follow the temporal dependencies. This introduces significant overhead and limits training throughput. Existing optimizations for static GNNs are not directly applicable to MTGNNs due to differences in training paradigm, model architecture, and the absence of a memory module. Moreover, these optimizations do not effectively address the challenges posed by temporal dependencies, making them ineffective for MTGNN training. In this paper, we propose MSPipe, a general and efficient framework for memory-based TGNNs that maximizes training throughput while maintaining model accuracy. Our design specifically addresses the unique challenges associated with fetching and updating node memory states in MTGNNs by integrating staleness into the memory module. However, simply introducing a predefined staleness bound in the memory module to break temporal dependencies may lead to suboptimal performance and lack of generalizability across different models and datasets. To overcome this, we introduce an online pipeline scheduling algorithm in MSPipe that strategically breaks temporal dependencies with minimal staleness and delays memory fetching to obtain fresher memory states. This is achieved without stalling the MTGNN training stage or causing resource contention. Additionally, we design a staleness mitigation mechanism to enhance training convergence and model accuracy. Furthermore, we provide convergence analysis and demonstrate that MSPipe maintains the same convergence rate as vanilla sampling-based GNN training. Experimental results show that MSPipe achieves up to 2.45× speed-up without sacrificing accuracy, making it a promising solution for efficient MTGNN training. The implementation of our paper can be found at the following link: https://github.com/PeterSH6/MSPipe.
Graph partitioning aims to divide a graph into k disjoint subsets while optimizing a specific partitioning objective. The majority of formulations related to graph partitioning exhibit NP-hardness due to their combinatorial nature. Conventional methods, like approximation algorithms or heuristics, are designed for distinct partitioning objectives and fail to achieve generalization across other important partitioning objectives. Recently machine learning-based methods have been developed that learn directly from data. Further, these methods have a distinct advantage of utilizing node features that carry additional information. However, these methods assume differentiability of target partitioning objective functions and cannot generalize for an unknown number of partitions, i.e., they assume the number of partitions is provided in advance. In this study, we develop NeuroCut with two key innovations over previous methodologies. First, by leveraging a reinforcement learning-based framework over node representations derived from a graph neural network and positional features, NeuroCut can accommodate any optimization objective, even those with non-differentiable functions. Second, we decouple the parameter space and the partition count making NeuroCut inductive to any unseen number of partition, which is provided at query time. Through empirical evaluation, we demonstrate that NeuroCut excels in identifying high-quality partitions, showcases strong generalization across a wide spectrum of partitioning objectives, and exhibits strong generalization to unseen partition count.
Reimagining Graph Classification from a Prototype View with Optimal Transport: Algorithm and Theorem
Recently, Graph Neural Networks (GNNs) have achieved inspiring performances in graph classification tasks. However, the message passing mechanism in GNNs implicitly utilizes the topological information of the graph, which may lead to a potential loss of structural information. Furthermore, the graph classification decision process based on GNNs resembles a black box and lacks sufficient transparency. The non-linear classifier following the GNNs also defaults to the assumption that each class is represented by a single vector, thereby limiting the diversity of intra-class representations. To address these issues, we propose a novel prototype-based graph classification framework that introduces the Fused Gromov-Wasserstein (FGW) distance in Optimal Transport (OT) as the similarity measure. In this way, the model explicitly exploits the structural information on the graph through OT while leading to a more transparent and straightforward classification process. The introduction of prototypes also inherently addresses the issue of limited within-class representations. Besides, to alleviate the widely acknowledged computational complexity issue of FGW distance calculation, we devise a simple yet effective NN-based FGW distance approximator, which can enable full GPU training acceleration with a marginal performance loss. In theory, we analyze the generalization performance of the proposed method and derive an O (1 over N) generalization bound, where the proof techniques can be extended to a broader range of prototype-based classification frameworks. Experimental results show that the proposed framework achieves competitive and superior performance on several widely used graph classification benchmark datasets. The code is avaliable at https://github.com/ChnQ/PGOT.
BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization
PDF ↗Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to guide the trials of different hyperparameter configurations amongst a specific search space. However, many trials may encounter severe training problems, such as vanishing gradients and insufficient convergence, which can hardly be reflected by accuracy metrics in the early stages of the training and often result in poor performance. This leads to an inefficient optimization trajectory because the bad trials occupy considerable computation resources and reduce the probability of finding excellent hyperparameter configurations within a time limitation. In this paper, we propose Bad Trial Tackler (BTTackler), a novel HPO framework that introduces training diagnosis to identify training problems automatically and hence tackles bad trials. BTTackler diagnoses each trial by calculating a set of carefully designed quantified indicators and triggers early termination if any training problems are detected. Evaluations are performed on representative HPO tasks consisting of three classical deep neural networks (DNN) and four widely used HPO methods. To better quantify the effectiveness of an automated HPO method, we propose two new measurements based on accuracy and time consumption. Results show the advantage of BTTackler on two-fold: (1) it reduces 40.33% of time consumption to achieve the same accuracy comparable to baseline methods on average and (2) it conducts 44.5% more top-10 trials than baseline methods on average within a given time budget. We also released an open-source Python library that allows users to easily apply BTTackler to automated HPO processes with minimal code changes\footnotehttps://github.com/thuml/BTTackler.
Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory, aiming to reconcile fairness and utility within the framework of spectral graph learning. We explore the correlation between sensitive features and spectrum in GNNs, using theoretical analysis to delineate the similarity between original sensitive features and those after convolution under different spectra. Our analysis reveals a reduction in the impact of similarity when the eigenvectors associated with the largest magnitude eigenvalue exhibit directional similarity. Based on these theoretical insights, we propose FUGNN, a novel spectral graph learning approach that harmonizes the conflict between fairness and utility. FUGNN ensures algorithmic fairness and utility by truncating the spectrum and optimizing eigenvector distribution during the encoding process. The fairness-aware eigenvector selection reduces the impact of convolution on sensitive features while concurrently minimizing the sacrifice of utility. FUGNN further optimizes the distribution of eigenvectors through a transformer architecture. By incorporating the optimized spectrum into the graph convolution network, FUGNN effectively learns node representations. Experiments on six real-world datasets demonstrate the superiority of FUGNN over baseline methods. The codes are available at https://github.com/yushuowiki/FUGNN.
Although machine learning algorithms demonstrate impressive performance, their trustworthiness remains a critical issue, particularly concerning fairness when implemented in real-world applications. Many notions of group fairness aim to minimize disparities in performance across protected groups. However, it can inadvertently reduce performance in certain groups, leading to sub-optimal outcomes. In contrast, Min-max group fairness notion prioritizes the improvement for the worst-performing group, thereby advocating a utility-promoting approach to fairness. However, it has been proven that existing efforts to achieve Min-max fairness exhibit limited effectiveness. In response to this challenge, we leverage the recently proposed "Neural Collapse'' framework to re-examine Empirical Risk Minimization (ERM) training, specifically investigating the root causes of poor performance in minority groups. The layer-peeled model is employed to decompose a network into two parts: an encoder to learn latent representation, and a subsequent classifier, with a systematic characterization of their training behaviors being conducted. Our analysis reveals that while classifiers achieve maximum separation, the separability of representations is insufficient, particularly for minority groups. This indicates the sub-optimal performance in minority groups stems from less separable representations, rather than classifiers. To tackle this issue, we introduce a novel strategy that incorporates a frozen classifier to directly enhance representation. Furthermore, we introduce two easily implemented loss functions to guide the learning process. The experimental assessments carried out on real-world benchmark datasets spanning the domains of Computer Vision, Natural Language Processing, and Tabular data demonstrate that our approach outperforms existing state-of-the-art methods in promoting the Min-max fairness notion.