United States

Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic connections between fraudster and user greatly impacts detection performance, where the fraudsters tend to camouflage themselves by building more connections to benign users. Despite their promising performance, their label reliance limits its application in unsupervised scenarios; Additionally, accurately capturing complex and diverse heterophily patterns without labels poses a further challenge. Therefore, we propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD, which contains two essential components: a heterophily estimation module and an alignment-based fraud detection module. In the heterophily estimation module, we design a novel unsupervised heterophily metric called HALO, which captures the critical graph properties for GFD, enabling its outstanding ability to estimate heterophily with attributes. In the alignment-based fraud detection module, we develop a joint MLP-GNN architecture with ranking loss and asymmetric alignment loss. The ranking loss aligns the predicted fraud score with the relative order of HALO, providing an extra robustness guarantee by comparing heterophily between non-adjacent nodes. Moreover, the asymmetric alignment loss effectively utilizes structural information to alleviate the feature-smooth effects. Extensive experiments on six datasets demonstrate that HUGE consistently outperforms competitors, showcasing its effectiveness and robustness.

AAAI 2025

A Label-free Heterophily-guided Approach for Unsupervised Graph Fraud Detection

graph based machine learning

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



The metro flow in Urban Rail Transit Systems (URTS) differs from other urban traffic flows because it is characterized by: (1) highly predetermined scheduling; and (2) interactively dynamic dependencies over the fixed physical infrastructure that vary with spatiotemporal and environmental factors. Notwithstanding the advances in graph neural networks, existing efforts fail to fully capture the characteristics and complex spatiotemporal dynamics specific to metro flow, as the innate graph-aware interactions underlying a metro flow are frequently affected by an amalgamation of: intrinsic connectivity, environmental associations, and flow-activated correlation, which usually dynamically evolve over time while containing redundant signals. We propose $\textbf{ReDyNet}$, a novel $\textbf{Re}$sponsive $\textbf{Dy}$namic Graph Neural $\textbf{Net}$work to accurately understand the spatiotemporal dynamics of metro flow and external factors. Specifically, it employs a responsive mechanism that adapts to variations in metro flow and external influences, ensuring the construction of an appropriate dynamic graph. In addition, ReDyNet follows the merits of information bottleneck (IB) theory with redundancy disentanglement to enhance the clarity and precision of contextual spatial signals. Our experiments conducted on three real-world metro passenger flow datasets demonstrate that the proposed ReDyNet outperforms several representative baselines.

Responsive Dynamic Graph Disentanglement for Metro Flow Forecasting

This paper presents fast and exact methods for computing the probability of acceptance of an argument using Dung's semantics in the Constellation paradigm of Abstract Argumentation. 
For (directed) Singly-Connected Graphs (SCGs), the problem can now be solved in linearithmic time instead of being exponential in the number of attacks, as reported in the literature.  
Moreover, in the more general case of (directed) Acyclic Graphs (AGs), we provide an algorithm whose time complexity is linearithmic in the product of the out-degree of dependent arguments, i.e., arguments reaching the argument considered for acceptance through multiple paths in the graph. 
We theoretically show that this complexity is lower than the lower-bound of the (exact) Constellation method, which is also supported by empirical results. 
We also compare our approach on AGs with the (approximate) Monte-Carlo method, which is stopped when our approach obtains the exact results. Within this time constraint, Monte-Carlo still outputs significant errors, underlying the fast computation of our approach.

Fast Computing of Dung Semantics in Acyclic Probabilistic Argumentation Frameworks

Decision-focused learning (DFL) offers an end-to-end approach to the predict-then-optimize (PO) framework by training predictive models directly on decision loss (DL), enhancing decision-making performance within PO contexts.
However, the implementation of DFL poses distinct challenges. Primarily, the decision loss can result in deviation from the physical significance of the predictions under limited data. Additionally, some prediction models are non-differentiable or black-box, which cannot be adjusted using gradient-based methods.

To tackle those challenges, we propose a novel framework, Decision-Focused Fine-tuning (DFF), which embeds the DFL module into the PO pipeline via a novel bias correction module. DFF is formulated as a constrained optimization problem that maintains the proximity of the DL-enhanced model to the original prediction model within a defined trust region. We theoretically prove that DFF strictly confines prediction bias within a predetermined upper bound, even with limited datasets, thereby substantially reducing prediction shifts caused by DL under limited data. Furthermore, the bias correction module can be integrated into diverse prediction models, enhancing adaptability to a broad range of PO tasks. Extensive evaluations on synthetic and real-world datasets, including network flow, portfolio optimization, and resource allocation problems with different prediction models, demonstrate that DFF not only improves decision performance but also adheres to fine-tuning constraints, showcasing robust adaptability across various scenarios.

DFF: Decision-Focused Fine-Tuning for Smarter Predict-Then-Optimize with Limited Data

Even though data annotation is extremely important for interpretability, research, and development of artificial intelligence solutions, annotating data remains costly. Research efforts such as active learning or few-shot learning alleviate the cost by increasing sample efficiency, yet the problem of annotating data more quickly has received comparatively little attention. Leveraging a predictor has been shown to reduce annotation cost in practice but has not been theoretically considered. We ask the following question: to annotate a binary classification dataset with $N$ samples, can the annotator answer less than $N$ yes/no questions? Framing this question-and-answer (Q\&A) game as an optimal encoding problem, we find a positive answer given by the Huffman encoding of the possible labelings. Unfortunately, the algorithm is computationally intractable even for small dataset sizes. As a practical method, we propose to minimize a cost function a few steps ahead, similarly to lookahead minimization in optimal control. This solution is analyzed, compared with the optimal one, and evaluated using several synthetic and real-world datasets. The method allows a significant improvement ($23-86\%$) in the annotation efficiency of real-world datasets.

Optimal and Efficient Binary Questioning for Accelerated Annotation

Group Equivariant Convolution (GConv) can effectively handle rotational symmetry data.
They assume uniform and strict rotational symmetry across all features, as the transformations under the specific group. 
However, real-world data rarely conforms to strict rotational symmetry commonly referred to as Rotational Symmetry-Breaking in the system or dataset, making GConv unable to adapt effectively to this phenomenon. 
Motivated by this, we propose a simple but highly effective method to address this problem, which utilizes a set of learnable biases called the $G$-Biases under the group order to break strict group constraints and achieve $\textbf{R}$elaxed $\textbf{R}$otational $\textbf{E}$quivarant $\textbf{Conv}$olution (RREConv). 
We conduct extensive experiments to validate Relaxed Rotational Equivariance on rotational symmetry groups $\mathcal{C}_n$ (e.g. $\mathcal{C}_2$, $\mathcal{C}_4$, and $\mathcal{C}_6$ groups). 
Further experiments demonstrate that our proposed RREConv-based methods achieve excellent performance, compared to existing GConv-based methods in classification and detection tasks on natural image datasets.

Relaxed Rotational Equivariance via G-Biases in Vision

In decision-making problem under uncertainty, predicting unknown parameters is often considered independent of the optimization part.
Decision-focused Learning (DFL) is a task-oriented framework to integrate prediction and optimization by adapting predictive model to give better decision for the corresponding task.
Here, an inevitable challenge arises when computing gradients of the optimal decision with respect to the parameters.
Existing researches cope this issue by smoothly reforming surrogate optimization or construct surrogate loss function that mimic task loss.
However, they are applied to restricted optimization domain.
In this paper, we propose Locally Convex Global Loss Network (LCGLN), a global surrogate loss model which can be implemented in a general DFL paradigm.
LCGLN learns task loss via partial input convex neural network which is guaranteed to be convex for chosen inputs, while keeping the non-convex global structure for the other inputs.
This enables LCGLN to admit general DFL through only a single surrogate loss without any sense for choosing appropriate parametric forms.
We confirm effectiveness and flexibility of LCGLN by evaluating our proposed model with three stochastic decision-making problems.

Locally Convex Global Loss Network for Decision-Focused Learning

Recently, foundational models have significantly advanced in different tasks, accompanied by Transformer as the general backbone. However, Transformer's quadratic complexity poses challenges for handling longer sequences and higher resolution images, which may limit foundational models further development. To alleviate this issue, various efficient State Space Models (SSMs) like Mamba have emerged, initially matching Transformer performance and gradually surpassing it. To improve the performance of SSMs in computer vision tasks, one crucial viewpoint is effective serialization of images. Existing vision Mambas, which rely on a linear scanning mechanism, often struggle to capture complex spatial relationships in 2D images. This results in feature loss during serialization and negatively impacts model performance. To overcome this limitation, we propose the use of fractal scanning curves for image serialization to enhance the Mambas’ ability to accurately model complex spatial dependencies. Additionally, unlike existing vision Mambas, which are designed with various curve scanning directions that increase the complexity, contradicting the original intent of Mamba to enhance model performance. We novelty introduce the Fractal Fusion Pathway (FFP) for our FractalMamba, which can enhance its performance efficiently. Extensive experiments underscore the superiority of our proposed FractalMamba.

Boosting Vision State Space Model with Fractal Scanning

Machine learning techniques are notably vulnerable to natural or adversarial perturbations, which can lead to catastrophic failures with significant economic, ethical, and societal risks. In this New Faculty Highlight Talk, I will showcase my research on harnessing robust statistics to build robust and trustworthy AI systems. Specifically, I will highlight my research breakthroughs in graph learning (GNNs), large language models (LLMs), deep equilibrium models (DEQs), and general deep representation learning. These breakthroughs stem from a unified and principled robust statistics framework that incorporates robustness as the core inductive bias in deep learning architecture. This approach has enabled significant improvements in intrinsic robustness and generalization, even in complex and challenging environments. My research demonstrates the transformative potential of harnessing robust statistics in enhancing the robustness and trustworthiness of AI systems. Looking forward, I will continue to push this frontier by advocating the design of robustness-informed neural networks across various areas.

Harnessing Robust Statistics for Trustworthy AI

Transfer learning aims to transfer knowledge or information from a source domain to a relevant target domain. It involves two key challenges: distribution shifts and trustworthiness concerns. Having these challenges in mind, my research focuses on understanding transfer learning from the perspective of knowledge transferability (e.g., IID and non-IID learning tasks) and trustworthiness (e.g., adversarial robustness, data privacy, and performance fairness).

Towards Trustworthy Machine Learning Under Distribution Shifts

Along with the broad deployment of deep learning (DL) systems, their lack of trustworthiness, such as their lack of robustness, fairness, and numerical reliability, is raising serious social concerns, especially in safety-critical scenarios such as autonomous driving and aircraft navigation. Hence, a rigorous and accurate evaluation of the trustworthiness of DL systems is essential and would be a prerequisite for improving DL trustworthiness. The first part of the talk will be an overview of certified methods for DL trustworthiness. These methods provide computable guarantees for DL systems in terms of worst-case trustworthiness under certain realistic conditions, such as the accuracy lower bound against arbitrary tiny perturbations. Based on our taxonomy and systematization, we illustrate key methodologies, specifically semantic randomized smoothing and branch-and-bound, and their implications for certified DL trustworthiness.

As a representative of recent DL breakthroughs, large language models (LLMs) are transforming our lives, but, on the other hand, posing more challenges to trustworthiness. For example, LLMs can be jailbroken with adversarial prompts to output harmful content with bias, harassment, misinformation, and more. The second part of the talk will be an overview of LLM trustworthiness. We will start with sharing hands-on experience in developing fontier LLMs, then illustrate common LLM trustworthiness issues via examples, then demonstrate evaluation challenges, take one benchmark as an example, and conclude by envisioning certifiable trustworthiness for LLMs.

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