OpenRaiser/Intern-Atlas huggingface dataset:
SGES: A General and Space-efficient Framework for Graphlet Counting in Graph Streams
Authors: Chen Yang, Lailong Luo, Yuliang Lu, Chu Huang, Qianzhen Zhang, Guozheng Yang, Deke Guo
Venue: CIKM
Year: 2024

Graphlets are small, connected, and non-isomorphic induced subgraphs that describe the topological structure of a graph. Counting graphlets is a fundamental task in graph mining and social network analysis. It has numerous applications in many fields, including dense subgraph discovery, anomaly detection, etc. Most existing work assumes a static graph. However, graphs are dynamic in the real world, which can be described as graph streams. Counting graphlets in graph streams is a challenge due to the streaming nature of the input. While there have been several studies on counting graphlets in graph streams, these works are limited to simple graphlets like triangles and butterflies. In this paper, we propose SGES algorithm to estimate more complex graphlets in graph streams. In SGES, we first propose an unbiased sampling strategy to maintain fixed-size sampled edges, which in turn allows us to unbiasedly estimate the number of subgraphs and then count graphlets based on the combinational relationship between the number of subgraphs and the number of graphlets. Extensive experiments over large real-world graph streams prove that our algorithm can obtain accurate estimation values of graphlet counts with high throughput.
Contrastive Disentangled Representation Learning for Debiasing Recommendation with Uniform Data
Authors: Xinxin Yang, Zhen Liu, Xiaoman Lu, Yafan Yuan, Sibo Lu, Yibo Gao
Venue: CIKM
Year: 2024

In recommender systems, learning high-quality user and item representations is crucial for predicting user preferences. However, there are various confounding factors in observational data, resulting in data bias, which hinders the learning of user and item representations. Recent work proposed to use uniform data to alleviate bias problem. However, these methods fail to learn pure representations for unbiased prediction, which are not affected by confounding factors. This paper introduces a novel disentangled framework, named CDLRec, for learning unbiased representations, leveraging uniform data as supervisory signal for disentangling. Furthermore, to address the scarcity problem of uniform data, the contrastive learning is utilized to implement disentanglement by providing augmented samples. Specifically, two contrastive strategies are designed based on different sampling ways for positives and negatives. Extensive experiments are conducted over two real-world datasets and the results demonstrate the superior performance of our proposed method.
You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning
Authors: Tianmeng Yang, Jiahao Meng, Min Zhou, Yaming Yang, Yujing Wang, Xiangtai Li, Yunhai Tong
Venue: CIKM
Year: 2024

Recent research on the robustness of Graph Neural Networks (GNNs) under noises or attacks has attracted great attention due to its importance in real-world applications. Most previous methods explore a single noise source, recovering corrupt node embedding by reliable structures bias or developing structure learning with reliable node features. However, the noises and attacks may come from both structures and features in graphs, making the graph denoising a dilemma and challenging problem. In this paper, we develop a unified graph denoising (UGD) framework to unravel the deadlock between structure and feature denoising. Specifically, a high-order neighborhood proximity evaluation method is proposed to recognize noisy edges, considering features may be perturbed simultaneously. Moreover, we propose to refine noisy features with reconstruction based on a graph auto-encoder. An iterative updating algorithm is further designed to optimize the framework and acquire a clean graph, thus enabling robust graph learning for downstream tasks. Our UGD framework is self-supervised and can be easily implemented as a plug-and-play module. We carry out extensive experiments, which proves the effectiveness and advantages of our method. Code is avalaible at https://github.com/YoungTimmy/UGD.
TrafCL: Robust Encrypted Malicious Traffic Detection via Contrastive Learning
Authors: Xiaodu Yang, Sijie Ruan, Jinyu Li, Yinliang Yue, Bo Sun
Venue: CIKM
Year: 2024

Remote control malwares enable cyber attackers to achieve command and control over victim hosts, which are widely employed in ransomware attacks and espionage operations, jeopardizing personal privacy and state security. To effectively detect such malicious traffics holds high practical value. However, prior works have not adequately addressed the task due to challenges of encrypted traffics with misleading contents, incomplete sessions, and limited labels. To overcome these limitations, in this paper, we propose TrafCL, a contrastive learning framework for robust encrypted malicious traffic detection. In TrafCL, we first generate incomplete variants for the input session by Session Augmentation, then extract explicit session features with excluding misleading traffic contents by Triple-aspect Session Feature Extraction, and obtain session representations by Co-attention Session Encoder which fuses triple-aspect session features with capturing their interdependence. After that, we use a projection head to obtain final representations. TrafCL is pre-trained using unlabeled data to learn close representations for complete sessions and their incomplete variants, then fine-tuned on labeled data to detect encrypted malicious traffics. Experiment results show that TrafCL outperforms the best baseline by 11.35% and 6.71% in F1-scores on two datasets respectively.
Behavior-Aware Hypergraph Convolutional Network for Illegal Parking Prediction with Multi-Source Contextual Information
Authors: Guang Yang, Meiqi Tu, Zelong Li, Jinquan Hang, Taichi Liu, Ruofeng Liu, Yi Ding, Yu Yang, Desheng Zhang
Venue: CIKM
Year: 2024

Illegal parking prediction is a crucial problem to help stakeholders with better urban planning and management. Existing works advance the field by capturing complex traffic correlations from spatial and temporal perspectives using deep learning models, and achieve state-of-the-art performance. However, current works do not consider the unique perspective from the illegal parking data collection process carried out by patrol officers, which can reflect a wealth of knowledge gained from each officer's on-the-ground experiences for more effective patrol. In this paper, we propose a novel behavior-aware hypergraph convolutional network named BHIPP for city-wide illegal parking prediction. To better represent the correlations of illegal parking events from patrol officers' perspective, we construct a new patrol hypergraph integrating patrol officers' experience alongsie multi-source contextual information. Additionally, we design a behavior-aware hypergraph convolutional network, which captures the complex and high-order illegal parking event correlations with officers' patrol behaviors explicitly considered. Further, we introduce a spatial-temporal illegal parking approximation module to estimate parking violations in under-patrolled regions using both historical and multi-source contextual data. Extensive experiments on real-world datasets demonstrate the superiority of our proposed BHIPP compared with a broad range of state-of-the-art baseline models across varying spatial-temporal granularities, from both regression and ranking aspects.
Leveraging Local Structure for Improving Model Explanations: An Information Propagation Approach
Authors: Ruo Yang, Binghui Wang, Mustafa Bilgic
Venue: CIKM
Year: 2024

Numerous explanation methods have been recently developed to interpret the decisions made by deep neural network (DNN) models. For image classifiers, these methods typically provide an attribution score to each pixel in the image to quantify its contribution to the prediction. However, most of these explanation methods appropriate attribution scores to pixels independently, even though both humans and DNNs make decisions by analyzing a set of closely related pixels simultaneously. Hence, the attribution score of a pixel should be evaluated jointly by considering itself and its structurally-similar pixels. We propose a method called IProp, which models each pixel's individual attribution score as a source of explanatory information and explains the image prediction through the dynamic propagation of information across all pixels. To formulate the information propagation, IProp adopts the Markov Reward Process, which guarantees convergence, and the final status indicates the desired pixels' attribution scores. Furthermore, IProp is compatible with any existing attribution-based explanation method. Extensive experiments on various explanation methods and DNN models verify that IProp significantly improves them on a variety of interpretability metrics.
The Effect of Icon Semantic Distance on Preschool Children's Information Search: Evidence from an Eye-Tracking Study
Authors: Jiaqi Yang, Pianran Wang
Venue: CIKM
Year: 2024

Icons are frequently employed in children-oriented information systems due to children's limited literacy. However, the inherent semantic distances of icons, which may influence their affordance to children, are often overlooked in the development of such systems and related research. In this study, we apply semantic distance to measure the explicitness of icons in children-oriented book search, utilizing self-developed icons tailored for indexing picture books. We first gathered data from children through questionnaires to assess the perceived semantic distance of each icon. Subsequently, we conducted eye-tracking experiments with 50 preschool children, measuring their search accuracy, response time, and eye movement patterns while using icons to locate specific picture books. Our findings indicate that preschool children are easier to use icons with close semantic distance and single icons for searching. Additionally, the ability to use icons with distant semantic distances and combination icons significantly improves with age. These findings may contribute to the development of more effective and children-friendly information search systems.
ST-ECP: A Novel Spatial-Temporal Framework for Energy Consumption Prediction of Vehicle Trajectory
Authors: Biao Yang, Yun Xiong, Xi Chen, Xuejing Feng, Meng Wang, Jun Ma
Venue: CIKM
Year: 2024

Accurately predicting Vehicle Energy Consumption (VEC) is crucial for estimating a vehicle's total energy requirements along a predetermined trajectory. Current research mainly focuses on personalized models that enhance VEC prediction accuracy by leveraging driving behavior features extracted from historical trajectory data. However, there are still two significant limitations. First, existing algorithms predominantly model trajectories with coarse granularity, focusing solely on overall characteristics and neglecting the crucial interplay between vehicles, drivers, and the environments, which fundamentally shape trajectory dynamics. Second, current models predict driver behavior preferences solely from vehicle operational states in historical trajectories, often overlooking the influence of external environmental factors. To overcome these limitations, we introduce a Spatial-Temporal Framework for Energy Consumption Prediction of Vehicle Trajectories (ST-ECP). Specifically, we construct a heterogeneous interaction graph that captures the complex relationships between vehicles, environments, and drivers, effectively characterizing the dynamic attributes of trajectories across various conditions. Additionally, we design a personalized pattern aggregation module to extract personalized driving behavior features. Extensive experimental on real-world datasets demonstrate the effectiveness and efficiency of ST-ECP.
MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions
Authors: Qinchen Yang, Zejun Xie, Hua Wei, Desheng Zhang, Yu Yang
Venue: CIKM
Year: 2024

Urban traffic is subject to disruptions that cause extended waiting time and safety issues at signalized intersections. While numerous studies have addressed the issue of intelligent traffic systems in the context of various disturbances, traffic signal malfunction, a common real-world occurrence with significant repercussions, has received comparatively limited attention. The primary objective of this research is to mitigate the adverse effects of traffic signal malfunction, such as traffic congestion and collision, by optimizing the control of neighboring functioning signals. To achieve this goal, this paper presents a novel traffic signal control framework (MalLight), which leverages an Influence-aware State Aggregation Module (ISAM) and an Influence-aware Reward Aggregation Module (IRAM) to achieve coordinated control of surrounding traffic signals. To the best of our knowledge, this study pioneers the application of a Reinforcement Learning(RL)-based approach to address the challenges posed by traffic signal malfunction. Empirical investigations conducted on real-world datasets substantiate the superior performance of our proposed methodology over conventional and deep learning-based alternatives in the presence of signal malfunction, with reduction of throughput alleviated by as much as 48.6%.
What a Surprise! Computing Rewritten Modules Can Be as Efficient as Computing Subset Modules
Authors: Zhihao Yang, Yizheng Zhao
Venue: CIKM
Year: 2024

Uniform Interpolation (UI) is an advanced non-standard reasoning service that seeks to refine ontologies by creating rewritten modules. These modules, known as uniform interpolants, retain only "relevant names" while preserving their meanings in the absence of other names. UI holds significant potential across various domains where tailored ontology modules are required. However, realizing its full potential demands highly optimized techniques for generating such modules. Previous studies have identified notable challenges in generating uniform interpolants for EL-ontologies, where their computation is substantially more complex and computationally demanding than standard subset modules. Despite these obstacles, this paper introduces an advanced "forgetting" method tailored for computing uniform interpolants of ELIO-ontologies with ABoxes. We show that with effective normalization and inference strategies, these uniform interpolants can be computed efficiently, matching the speed of standard module computation. A comprehensive evaluation using a prototype implementation of this method achieved a 100% success rate on two major benchmark datasets, Oxford-ISG and BioPortal, with results delivered within seconds. The efficiency of our approach is attributed to our novel linear strategy for introducing definers, in sharp contrast to existing strategies that lead to an exponential increase in definers and computational inefficiency. Our method is unique in its ability to create signature-restricted modules for large-scale ontologies, making it a vital addition to the community's toolkit.
Efficient Pruned Top-K Subgraph Matching with Topology-Aware Bounds
Authors: Linglin Yang, Yuqi Zhou, Yue Pang, Lei Zou
Venue: CIKM
Year: 2024

Given a query graph, top-k subgraph matching finds up to k matches in a data graph with the highest scores according to a user-defined scoring function. It has wide applications across many fields, including knowledge graphs and social networks. Due to the enormous search space, existing methods are not efficient enough on large graphs. In this paper, we propose PTAB, an efficient algorithm for top-k subgraph matching. It traverses an efficiently pruned search space by topology-aware sub-space score upper bounds computed from a novel hop index, which stores the range of node properties in a constrained multi-hop neighborhood of each node. Additionally, PTAB integrates a cost-aware root selection strategy, which chooses query nodes leading to a search process that utilizes the pruning power of the hop index as much as possible. Furthermore, we use a novel edge-cut strategy to handle general query graphs with cycles. Experimental results on real and synthetic datasets demonstrate that our method outperforms existing methods.
Decoupled Behavior-based Contrastive Recommendation
Authors: Mengduo Yang, Jie Zhou, Meng Xi, Xiaohua Pan, Yi Yuan, Ying Li, Yangyang Wu, Jinshan Zhang, Jianwei Yin
Venue: CIKM
Year: 2024

Recommender systems are crucial tools in online applications, assisting users in discovering relevant content efficiently. Recent studies demonstrate that contrastive learning (CL) based methods yield significant results in collaborative filtering (CF) recommendations, due to their ability to address the issue of data sparsity. However, two inherent limitations remain unexplored in these methods. a) Since the datasets commonly used are binary (0: no interaction; 1: interaction), current methods only provide rudimentary modeling of user behaviors in binary form, which fails to model complex user-item interactions and relationships in real-world recommendation scenarios. b) Existing CL-based methods mostly construct contrastive views through heuristic-based embedding or structure perturbation, which are prone to introduce noise or discard important information, leading to a decreased representation quality. To address these issues, we propose a Decoupled Behavior-based Contrastive Recommendation model (DBCR) that effectively decouples user behaviors from binary datasets for better user-item interaction modeling. The core idea is to decouple latent user behaviors from unlabelled user-item interactions (binary datasets) and utilize self-supervised contrastive learning to optimize CF-based recommendation jointly. Specifically, we introduce latent behavior variables and embed them into user-item interaction modeling within the generalized expectation-maximization (EM) framework. Moreover, we design a contrastive learning task by constructing a preference view instead of unreasonable perturbation to further improve the learned representation. Experimental results and analyses on three real-world datasets demonstrate the effectiveness of DBCR and its high efficiency, with an average improvement of 16.9% over state-of-the-art methods. Our code is available on https://github.com/Du-danger/DBCR.
Combining Incomplete Observational and Randomized Data for Heterogeneous Treatment Effects
Authors: Dong Yao, Caizhi Tang, Qing Cui, Longfei Li
Venue: CIKM
Year: 2024

Data from observational studies (OSs) is widely available and readily obtainable yet frequently contains confounding biases. On the other hand, data derived from randomized controlled trials (RCTs) helps to reduce these biases; however, it is expensive to gather, resulting in a tiny size of randomized data. For this reason, effectively fusing observational data and randomized data to better estimate heterogeneous treatment effects (HTEs) has gained increasing attention. However, existing methods for integrating observational data with randomized data must require complete observational data, meaning that both treated subjects and untreated subjects must be included in OSs. This prerequisite confines the applicability of such methods to very specific situations, given that including all subjects, whether treated or untreated, in observational studies is not consistently achievable. In our paper, we propose a resilient approach to Combine Incomplete Observational data and randomized data for HTE estimation, which we abbreviate as CIO. The CIO is capable of estimating HTEs efficiently regardless of the completeness of the observational data, be it full or partial. Concretely, a confounding bias function is first derived using the pseudo-experimental group from OSs, in conjunction with the pseudo-control group from RCTs, via an effect estimation procedure. This function is subsequently utilized as a corrective residual to rectify the observed outcomes of observational data during the HTE estimation by combining the available observational data and the all randomized data. To validate our approach, we have conducted experiments on a synthetic dataset and two semi-synthetic datasets.
Towards a Zero-Day Anomaly Detector in Cyber Physical Systems Using a Hybrid VAE-LSTM-OCSVM Model
Authors: Romarick Yatagha, Betelhem Nebebe, Karl Waedt, Christoph Ruland
Venue: CIKM
Year: 2024

Despite the growing volume of time series data across various domains, detecting anomalies remains challenging due to the complexity and dynamic nature of the data. Traditional monitoring systems are inefficient in capturing contextual and temporal anomalies that are only viable through time and handling high-dimensional data. From implementation to deployment, this paper presents an anomaly detection system on a cyber-physical system by integrating Variational Autoencoders (VAE) with Long Short-Term Memory (LSTM) networks and One-Class Support Vector Machine (OCSVM), forming a hybrid VAE-LSTM-OCSVM model. The proposed architecture positions itself as a zero-day anomaly detector, by learning the nominal functioning of systems, enabling it to identify deviations from normal operations without prior knowledge of specific anomalies. This capability significantly enhances the model's utility in online monitoring, making it adept at detecting unforeseen operational disruptions. We propose an Adaptive Loss Weight Adjustment Algorithm (ALWAA) to account for Domain incremental learning in our system, as required by the ISO/IEC 42001:2023 and ISO/IEC 23053:2022 standards. The model is evaluated on a dataset including 2 types of anomalies, comparing and demonstrating its superiority over existing methods. The findings suggest that the hybrid VAE-LSTM-OCSVM model offers a promising direction for more effective and efficient anomaly detection in time series data, with its ability to safeguard against known and unknown anomalies.
Span Confusion is All You Need for Chinese Spelling Correction
Authors: Dezhi Ye, Haomei Jia, Bowen Tian, Jie Liu, Haijin Liang, Jin Ma, Wenmin Wang
Venue: CIKM
Year: 2024

Chinese Spelling Correction is to detect errors in a statement and correct them. Researchers have recently begun to investigate the use of pre-train language models to perform this task. They replace tokens with similar characters based on a confusion set rather than mask tokens when pre-training. A significant limitation of this method is that pre-training with single tokens cannot effectively simulate natural errors, such as full spelling. Furthermore, the confusion set consists of single characters, which lacks the contextual relationship between characters. This leads to a lower quality of generated pseudo-labeled samples. To this end, we contribute a novel approach of constructing confusion corpus, which can automatically generate high-quality spelling errors. The construction of our confusion set is based on user behavior patterns, aiming to identify characters that are easily confused in similar contexts. Then we introduce a Span Confusion Pre-Train(SCPre) strategy for Chinese Spelling Correction. The span confusion strategy replaces characters, words, and phrases in the text according to a large confusion set. Upon the constructed corpus, different models are trained and evaluated for CSC with respect to real-world datasets. Quantitative experiments on datasets show that our approach empirically achieves unprecedented performance.