Author name cluster

Yinglong Xia

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

S'MoRE: Structural Mixture of Residual Experts for Parameter-Efficient LLM Fine-tuning

Hanqing Zeng
Yinglong Xia
Zhuokai Zhao
Chuan Jiang
Qiang Zhang
Jiayi Liu
Qunshu Zhang
Lizhu Zhang

Fine-tuning pre-trained large language models (LLMs) presents a dual challenge of balancing parameter efficiency and model capacity. Existing methods like low-rank adaptations (LoRA) are efficient but lack flexibility, while Mixture-of-Experts (MoE) enhance model capacity at the cost of more & under-utilized parameters. To address these limitations, we propose Structural Mixture of Residual Experts (S’MoRE), a novel framework that seamlessly integrates the efficiency of LoRA with the flexibility of MoE. Conceptually, S’MoRE employs hierarchical low-rank decomposition of expert weights, yielding residuals of varying orders interconnected in a multi-layer structure. By routing input tokens through sub-trees of residuals, S’MoRE emulates the capacity of numerous experts by instantiating and assembling just a few low-rank matrices. We craft the inter-layer propagation of S’MoRE’s residuals as a special type of Graph Neural Network (GNN), and prove that under similar parameter budget, S’MoRE improves structural flexibility of traditional MoE (or Mixture-of-LoRA) by exponential order. Comprehensive theoretical analysis and empirical results demonstrate that S’MoRE achieves superior fine-tuning performance, offering a transformative approach for efficient LLM adaptation. Our implementation is available at: https: //github. com/ZimpleX/SMoRE-LLM.

PDF Details

ICLR Conference 2024 Conference Paper

Deceptive Fairness Attacks on Graphs via Meta Learning

Jian Kang 0008
Yinglong Xia
Ross Maciejewski
Jiebo Luo 0001
Hanghang Tong

We study deceptive fairness attacks on graphs to answer the following question: How can we achieve poisoning attacks on a graph learning model to exacerbate the bias deceptively? We answer this question via a bi-level optimization problem and propose a meta learning-based framework named FATE. FATE is broadly applicable with respect to various fairness definitions and graph learning models, as well as arbitrary choices of manipulation operations. We further instantiate FATE to attack statistical parity or individual fairness on graph neural networks. We conduct extensive experimental evaluations on real-world datasets in the task of semi-supervised node classification. The experimental results demonstrate that FATE could amplify the bias of graph neural networks with or without fairness consideration while maintaining the utility on the downstream task. We hope this paper provides insights into the adversarial robustness of fair graph learning and can shed light on designing robust and fair graph learning in future studies.

Details

AAAI Conference 2024 Conference Paper

Hierarchical Multi-Marginal Optimal Transport for Network Alignment

Zhichen Zeng
Boxin Du
Si Zhang
Yinglong Xia
Zhining Liu
Hanghang Tong

Finding node correspondence across networks, namely multi-network alignment, is an essential prerequisite for joint learning on multiple networks. Despite great success in aligning networks in pairs, the literature on multi-network alignment is sparse due to the exponentially growing solution space and lack of high-order discrepancy measures. To fill this gap, we propose a hierarchical multi-marginal optimal transport framework named HOT for multi-network alignment. To handle the large solution space, multiple networks are decomposed into smaller aligned clusters via the fused Gromov-Wasserstein (FGW) barycenter. To depict high-order relationships across multiple networks, the FGW distance is generalized to the multi-marginal setting, based on which networks can be aligned jointly. A fast proximal point method is further developed with guaranteed convergence to a local optimum. Extensive experiments and analysis show that our proposed HOT achieves significant improvements over the state-of-the-art in both effectiveness and scalability.

PDF Details DOI

ICLR Conference 2024 Conference Paper

Mixture of Weak and Strong Experts on Graphs

Hanqing Zeng
Hanjia Lyu
Diyi Hu
Yinglong Xia
Jiebo Luo 0001

Realistic graphs contain both (1) rich self-features of nodes and (2) informative structures of neighborhoods, jointly handled by a Graph Neural Network (GNN) in the typical setup. We propose to decouple the two modalities by **M**ixture **o**f **w**eak and **st**rong experts (**Mowst**), where the weak expert is a light-weight Multi-layer Perceptron (MLP), and the strong expert is an off-the-shelf GNN. To adapt the experts' collaboration to different target nodes, we propose a "confidence" mechanism based on the dispersion of the weak expert's prediction logits. The strong expert is conditionally activated in the low-confidence region when either the node's classification relies on neighborhood information, or the weak expert has low model quality. We reveal interesting training dynamics by analyzing the influence of the confidence function on loss: our training algorithm encourages the specialization of each expert by effectively generating soft splitting of the graph. In addition, our "confidence" design imposes a desirable bias toward the strong expert to benefit from GNN's better generalization capability. Mowst is easy to optimize and achieves strong expressive power, with a computation cost comparable to a single GNN. Empirically, Mowst on 4 backbone GNN architectures show significant accuracy improvement on 6 standard node classification benchmarks, including both homophilous and heterophilous graphs (https://github.com/facebookresearch/mowst-gnn).

Details

ICML Conference 2023 Conference Paper

Generative Graph Dictionary Learning

Zhichen Zeng 0001
Ruike Zhu
Yinglong Xia
Hanqing Zeng
Hanghang Tong

Dictionary learning, which approximates data samples by a set of shared atoms, is a fundamental task in representation learning. However, dictionary learning over graphs, namely graph dictionary learning (GDL), is much more challenging than vectorial data as graphs lie in disparate metric spaces. The sparse literature on GDL formulates the problem from the reconstructive view and often learns linear graph embeddings with a high computational cost. In this paper, we propose a Fused Gromov-Wasserstein (FGW) Mixture Model named FraMe to address the GDL problem from the generative view. Equipped with the graph generation function based on the radial basis function kernel and FGW distance, FraMe generates nonlinear embedding spaces, which, as we theoretically proved, provide a good approximation of the original graph spaces. A fast solution is further proposed on top of the expectation-maximization algorithm with guaranteed convergence. Extensive experiments demonstrate the effectiveness of the obtained node and graph embeddings, and our algorithm achieves significant improvements over the state-of-the-art methods.

Details

ECAI Conference 2023 Conference Paper

User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations

Juntao Tan
Yingqiang Ge
Yan Zhu
Yinglong Xia
Jiebo Luo 0001
Jianchao Ji
Yongfeng Zhang 0003

Modern recommender systems utilize users’ historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminished user satisfaction and trust in the systems. Acknowledging the recent advancements in explainable recommender systems that enhance users’ understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability. In this paper, we present a user-controllable recommender system that seamlessly integrates explainability and controllability within a unified framework. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system by interacting with these explanations. Furthermore, we introduce and assess two attributes of controllability in recommendation systems: the complexity of controllability and the accuracy of controllability. Experimental evaluations on MovieLens and Yelp datasets substantiate the effectiveness of our proposed framework. Additionally, our experiments demonstrate that offering users control options can potentially enhance recommendation accuracy in the future. Source code and data are available at https: //github. com/chrisjtan/ucr.

Details

AAAI Conference 2022 Conference Paper

Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning

Yuheng Zhang
Hanghang Tong
Yinglong Xia
Yan Zhu
Yuejie Chi
Lei Ying

Graph neural networks (GNNs) have achieved tremendous success in many graph learning tasks such as node classification, graph classification and link prediction. For the classification task, GNNs’ performance often highly depends on the number of labeled nodes and thus could be significantly hampered due to the expensive annotation cost. The sparse literature on active learning for GNNs has primarily focused on selecting only one sample each iteration, which becomes inefficient for large scale datasets. In this paper, we study the batch active learning setting for GNNs where the learning agent can acquire labels of multiple samples at each time. We formulate batch active learning as a cooperative multi-agent reinforcement learning problem and present a novel reinforced batch-mode active learning framework (BIGENE). To avoid the combinatorial explosion of the joint action space, we introduce a value decomposition method that factorizes the total Q-value into the average of individual Q-values. Moreover, we propose a novel multi-agent Q-network consisting of a graph convolutional network (GCN) component and a gated recurrent unit (GRU) component. The GCN component takes both the informativeness and inter-dependences between nodes into account and the GRU component enables the agent to consider interactions between selected nodes in the same batch. Experimental results on multiple public datasets demonstrate the effectiveness and efficiency of our proposed method.

PDF Details

NeurIPS Conference 2021 Conference Paper

Decoupling the Depth and Scope of Graph Neural Networks

Hanqing Zeng
Muhan Zhang
Yinglong Xia
Ajitesh Srivastava
Andrey Malevich
Rajgopal Kannan
Viktor Prasanna
Long Jin

State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes. On large graphs, increasing the model depth often means exponential expansion of the scope (i. e. , receptive field). Beyond just a few layers, two fundamental challenges emerge: 1. degraded expressivity due to oversmoothing, and 2. expensive computation due to neighborhood explosion. We propose a design principle to decouple the depth and scope of GNNs – to generate representation of a target entity (i. e. , a node or an edge), we first extract a localized subgraph as the bounded-size scope, and then apply a GNN of arbitrary depth on top of the subgraph. A properly extracted subgraph consists of a small number of critical neighbors, while excluding irrelevant ones. The GNN, no matter how deep it is, smooths the local neighborhood into informative representation rather than oversmoothing the global graph into “white noise”. Theoretically, decoupling improves the GNN expressive power from the perspectives of graph signal processing (GCN), function approximation (GraphSAGE) and topological learning (GIN). Empirically, on seven graphs (with up to 110M nodes) and six backbone GNN architectures, our design achieves significant accuracy improvement with orders of magnitude reduction in computation and hardware cost.

PDF Details

NeurIPS Conference 2021 Conference Paper

Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning

Muhan Zhang
Pan Li
Yinglong Xia
Kai Wang
Long Jin

In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such as link). We know that GNN is designed to learn single-node representations. When we want to learn a node set representation involving multiple nodes, a common practice in previous works is to directly aggregate the single-node representations obtained by a GNN into a joint node set representation. In this paper, we show a fundamental constraint of such an approach, namely the inability to capture the dependence between nodes in the node set, and argue that directly aggregating individual node representations does not lead to an effective joint representation for multiple nodes. Then, we notice that a few previous successful works for multi-node representation learning, including SEAL, Distance Encoding, and ID-GNN, all used node labeling. These methods first label nodes in the graph according to their relationships with the target node set before applying a GNN. Then, the node representations obtained in the labeled graph are aggregated into a node set representation. By investigating their inner mechanisms, we unify these node labeling techniques into a single and most general form---labeling trick. We prove that with labeling trick a sufficiently expressive GNN learns the most expressive node set representations, thus in principle solves any joint learning tasks over node sets. Experiments on one important two-node representation learning task, link prediction, verified our theory. Our work explains the superior performance of previous node-labeling-based methods, and establishes a theoretical foundation of using GNNs for multi-node representation learning.

PDF Details

UAI Conference 2012 Conference Paper

Fast Graph Construction Using Auction Algorithm

Jun Wang
Yinglong Xia

In practical machine learning systems, graph based data representation has been widely used in various learning paradigms, ranging from unsupervised clustering to supervised classiﬁcation. Besides those applications with natural graph or network structure data, such as social network analysis and relational learning, many other applications often involve a critical step in converting data vectors to an adjacency graph. In particular, a sparse subgraph extracted from the original graph is often required due to both theoretic and practical needs. Previous study clearly shows that the performance of diﬀerent learning algorithms, e.g., clustering and classiﬁcation, beneﬁts from such sparse subgraphs with balanced node connectivity. However, the existing graph construction methods are either computationally expensive or with unsatisfactory performance. In this paper, we utilize a scalable method called auction algorithm and its parallel extension to recover a sparse yet nearly balanced subgraph with signiﬁcantly reduced computational cost. Empirical study and comparison with the stateof-art approaches clearly demonstrate the superiority of the proposed method in both ef- ﬁciency and accuracy.

Details