Explainable transaction risk analysis is a challenge for traditional deep learning models, which only predict suspicious transactions without explanations. Current explainable methods rely on hand-crafted rules and lack the ability to automatically generate language-based explanations. Large Language Models (LLMs) offer promise due to their reasoning and text generation abilities but struggle with domain knowledge and hallucinations, making risk analysis difficult. Specifically, LLMs face: (1) insufficient adaptation to transaction data analysis, and (2) ineffective knowledge retrieval methods that ignore the rich graph structure of transaction data. To address these issues, we propose the Dual Graph Retrieval-Augmented Generation (Dual-gRAG) framework, which utilizes dual retrieval: expert knowledge and reasoning case retrieval. Expert knowledge compensates for domain gaps, while reasoning case retrieval provides step-wise analysis guidance. We incorporate both graph-structured features and semantic features into the retrieval process to enhance the effectiveness of the retrieval. Extensive experiments show that Dual-gRAG improves LLMs' risk analysis capabilities, achieving a 15% increase in different metrics.

Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations and notable for their ease of data acquisition, have garnered considerable attention from both academic researchers and industry practitioners. The representation learning of DTDGs has been extensively applied to model the dynamics of temporally changing entities and their evolving connections. Currently, DTDG representation learning predominantly relies on GNN+RNN architectures, which manifest the inherent limitations of both Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs). GNNs suffer from the over-smoothing issue as the models architecture goes deeper, while RNNs struggle to capture long-term dependencies effectively. GNN+RNN architectures also grapple with scaling to large graph sizes and long sequences. Additionally, these methods often compute node representations separately and focus solely on individual node characteristics, thereby overlooking the behavior intersections between the two nodes whose link is being predicted, such as instances where the two nodes appear together in the same context or share common neighbors.

Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations. This paper investigates the structural vulnerabilities in graph neural models where sensitive topological information can be inferred through edge reconstruction attacks. Our research primarily addresses the theoretical underpinnings of similarity-based edge reconstruction attacks (SERA), furnishing a non-asymptotic analysis of their reconstruction capacities. Moreover, we present empirical corroboration indicating that such attacks can perfectly reconstruct sparse graphs as graph size increases. Conversely, we establish that sparsity is a critical factor for SERA's effectiveness, as demonstrated through analysis and experiments on (dense) stochastic block models. Finally, we explore the resilience of private graph representations produced via noisy aggregation (NAG) mechanism against SERA. Through theoretical analysis and empirical assessments, we affirm the mitigation of SERA using NAG . In parallel, we also empirically delineate instances wherein SERA demonstrates both efficacy and deficiency in its capacity to function as an instrument for elucidating the trade-off between privacy and utility.

Mobile apps have become an indispensable part of people’s daily lives. Users determine what apps to use and when and where to use them based on their tastes, interests, and personal demands, depending on their personality traits. This article aims to infer user profiles from their spatiotemporal mobile app usage behavior. Specifically, we first transform mobile app usage records into a heterogeneous graph. On the graph, nodes represent users, apps, locations, and time slots. Edges describe the co-occurrence of entities in usage records. We then develop a multi-relational heterogeneous graph attention network (MRel-HGAN), an end-to-end system for user profiling. MRel-HGAN first adopts a neighbor sampling strategy based on bootstrapping to sample heavily connected neighbors of a fixed size for each node. Next, we design a relational graph convolutional operation and a multi-relational attention operation. Through such modules, MRel-HGAN can generate node embedding by sufficiently leveraging the rich semantic information of the multi-relational structure in the mobile app usage graph. Experimental results on real-world mobile app usage datasets show the effectiveness and superiority of our MRel-HGAN in the user profiling task for attributes of gender and age.

Urban abnormal events constitute a significant threat to social order and public safety. It is of vital importance for emergency treatment if the location and time of abnormal events could be predicted before they happen. However, forecasting the occurrence of urban abnormal events is extremely challenging due to various influencing factors. First, the spatiotemporal environment in urban space is associated with complicated and dynamic attributes, which all potentially affect the happening of urban emergency events. Second, historical events also influence the occurrence of future events, and the impacts vary across urban regions and time due to dynamic regional relations. In this paper, we propose a framework called CityNeuro that incorporates both environmental and historical influence for location and time prediction of urban abnormal events. On the one hand, we identify important environmental factors by analyzing real-world datasets and constructing essential spatiotemporal features accordingly. On the other hand, we propose using neural region states to capture important historical information with a novel spatiotemporal information propagation mechanism. To the best of our knowledge, we are the first to forecast the precise location and time of individual urban abnormal events. Extensive experiments on real-world datasets demonstrate the advantages of our model compared with state-of-the-art spatiotemporal prediction methods.

Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening is of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.

Online services are playing critical roles in almost all aspects of users’ life. Users usually have multiple online identities (IDs) in different online services. In order to fuse the separated user data in multiple services for better business intelligence, it is critical for service providers to link online IDs belonging to the same user. On the other hand, the popularity of mobile networks and GPS-equipped smart devices have provided a generic way to link IDs, i.e., utilizing themobility tracesof IDs. However, linking IDs based on their mobility traces has been a challenging problem due to the highly heterogeneous, incomplete and noisy mobility data across services. In this paper, we proposeDPLink, an end-to-end deep learning based framework, to complete the user identity linkage task for heterogeneous mobility data collected from different services with different properties.DPLinkis made up by afeature extractorincluding a location encoder and a trajectory encoder to extract representative features from trajectory and acomparatorto compare and decide whether to link two trajectories as the same user. Particularly, we propose a pre-training strategy with a simple task to train theDPLinkmodel to overcome the training difficulties introduced by the highly heterogeneous nature of different source mobility data. Besides, we introduce a multi-modal embedding network and a co-attention mechanism inDPLinkto deal with the low-quality problem of mobility data. By conducting extensive experiments on two real-life ground-truth mobility datasets with eight baselines, we demonstrate thatDPLinkoutperforms the state-of-the-art solutions by more than 15 percent in terms of hit-precision. Moreover, it is expandable to add external geographical context data and works stably with heterogeneous noisy mobility traces.

Traffic prediction is a crucial task in many real-world applications. The task is challenging due to the implicit and dynamic spatio-temporal dependencies among traffic data. On the one hand, the spatial dependencies among traffic flows are latent and fluctuate with environmental conditions. On the other hand, the temporal dependencies among traffic flows also vary significantly over time and locations. In this paper, we propose Adaptive Spatio-Temporal Convolutional Network (ASTCN) to tackle these challenges. First, we propose a spatial graph learning module that learns the dynamic spatial relations among traffic data based on multiple influential factors. Furthermore, we design an adaptive temporal convolution module that captures complex temporal traffic dependencies with environment-aware dynamic filters. We conduct extensive experiments on three real-world traffic datasets. The results demonstrate that the proposed ASTCN consistently outperforms state-of-the-arts.

The outbreak of Covid-19 changed the world as well as human behavior. In this article, we study the impact of Covid-19 on smartphone usage. We gather smartphone usage records from a global data collection platform called Carat, including the usage of mobile users in North America from November 2019 to April 2020. We then conduct the first study on the differences in smartphone usage across the outbreak of Covid-19. We discover that Covid-19 leads to a decrease in users' smartphone engagement and network switches, but an increase in WiFi usage. Also, its outbreak causes new typical diurnal patterns of both memory usage and WiFi usage. Additionally, we investigate the correlations between smartphone usage and daily confirmed cases of Covid-19. The results reveal that memory usage, WiFi usage, and network switches of smartphones have significant correlations, whose absolute values of Pearson coefficients are greater than 0.8. Moreover, smartphone usage behavior has the strongest correlation with the Covid-19 cases occurring after it, which exhibits the potential of inferring outbreak status. By conducting extensive experiments, we demonstrate that for the inference of outbreak stages, both Macro-F1 and Micro-F1 can achieve over 0.8. Our findings explore the values of smartphone usage data for fighting against the epidemic.

The increasing amount of urban data enable us to investigate urban dynamics, assist urban planning, and eventually, make our cities more livable and sustainable. In this paper, we focus on learning an embedding space from urban data for urban regions. For the first time, we propose a multi-view joint learning model to learn comprehensive and representative urban region embeddings. We first model different types of region correlations based on both human mobility and inherent region properties. Then, we apply a graph attention mechanism in learning region representations from each view of the built correlations. Moreover, we introduce a joint learning module that boosts the region embedding learning by sharing cross-view information and fuses multi-view embeddings by learning adaptive weights. Finally, we exploit the learned embeddings in the downstream applications of land usage classification and crime prediction in urban areas with real-world data. Extensive experiment results demonstrate that by exploiting our proposed joint learning model, the performance is improved by a large margin on both tasks compared with the state-of-the-art methods.

The prevalence of smartphones has promoted the popularity of mobile apps in recent years. Although significant effort has been made to understand mobile app usage, existing studies are based primarily on short-term datasets with limited time span, e.g., a few months. Therefore, many basic facts about the long-term evolution of mobile app usage are unknown. In this paper, we study how mobile app usage evolves over a long-term period. We first introduce an app usage collection platform named carat, from which we have gathered app usage records of 1,465 users from 2012 to 2017. We then conduct the first study on the long-term evolution processes on a macro-level, i.e., app-category, and micro-level, i.e., individual app. We discover that, on both levels, there is a growth stage enabled by the introduction of new technologies. Then there is a plateau stage caused by high correlations between app categories and a pareto effect in individual app usage, respectively. Additionally, the evolution of individual app usage undergoes an elimination stage due to fierce intra-category competition. Nevertheless, the diverseness of app-category and individual app usage exhibit opposing trends: app-category usage assimilates while individual app usage diversifies. Our study provides useful implications for app developers, market intermediaries, and service providers.

Urban anomalies such as abnormal flow of crowds and traffic accidents could result in loss of life or property if not handled properly. Detecting urban anomalies at the early stage is important to minimize the adverse effects. However, urban anomaly detection is difficult due to two challenges: a) the criteria of urban anomalies varies with different locations and time; b) urban anomalies of different types may show different signs. In this paper, we propose a decomposing approach to address these two challenges. Specifically, we decompose urban dynamics into the normal component and the abnormal component. The normal component is merely decided by spatiotemporal features, while the abnormal component is caused by anomalous events. Then, we extract spatiotemporal features and estimate the normal component accordingly. At last, we derive the abnormal component to identify anomalies. We evaluate our method using both real-world and synthetic datasets. The results show our method can detect meaningful events and outperforms state-of-the-art anomaly detecting methods by a large margin.

Online services are playing critical roles in almost all aspects of users' life. Users usually have multiple online identities (IDs) in different online services. In order to fuse the separated user data in multiple services for better business intelligence, it is critical for service providers to link online IDs belonging to the same user. On the other hand, the popularity of mobile networks and GPS-equipped smart devices have provided a generic way to link IDs, i.e., utilizing the mobility traces of IDs. However, linking IDs based on their mobility traces has been a challenging problem due to the highly heterogeneous, incomplete and noisy mobility data across services.

Understanding the patterns of mobile data consumption is extremely valuable to reveal human activities and ecology in urban areas. This task is nontrivial in terms of three challenges: the complexity of mobile data consumption in large urban environment, the disturbance of abnormal events, and lack of prior knowledge for urban traffic patterns. We propose a novel approach to design a powerful system that consists of three subsystems: time series decomposing of mobile traffic data, extracting patterns from different components of the original traffic, and detecting anomalous events from noises. Our investigation involving the mobile traffic records of 6,400 cellular towers in Shanghai reveals three important observations. First, among all the 6,400 cellular towers, we identify five daily patterns corresponding to different human daily activity patterns. Second, we find that two natural patterns can be extracted from the weekly trend of mobile traffic consumption, which reflects modes of human activities. Last but not least, besides the regular patterns, we investigate how irregular activities affect mobile traffic consumption, and exploit this knowledge to successfully detect unusual events like concerts and soccer matches. Our proposed methodology therefore will aid a comprehensive understanding of large-scale mobile traffic consumption in urban areas.

The goal of this paper is to understand the patterns of mobile traffic consumption and reveal the correlations between human activities and mobile traffic patterns in the urban environment. This task is nontrivial in terms of three challenges: the complexity of mobile traffic consumption in large urban scale, the disturbance of abnormal events, and lack of prior knowledge about urban traffic patterns. We propose a novel approach and design a powerful system that consists of three parts: time series decomposing of mobile traffic data, extracting patterns from different components of the original traffic, and detecting anomalous events from noises. Our investigation reveals three important observations. Firstly, among all the 6,400 cellular towers we identify five daily patterns corresponding to different human daily activity patterns. Secondly, we find out that two natural patterns can be extracted from the weekly trend of mobile traffic consumption, which reflects modes of human activities from a different perspective. Last but not least, besides the regular patterns, we investigate how do irregular activities affect mobile traffic consumption, and exploit this knowledge to successfully detect unusual events like concerts and soccer matches. We believe our proposed methodology will lead to a comprehensive understanding of large-scale mobile traffic consumption in the urban areas.