Building a general model capable of analyzing human trajectories across different geographic regions and different tasks becomes an emergent yet important problem for various applications. However, existing works suffer from the generalization problem, i.e., they are either restricted to train for specific regions or only suitable for a few tasks. Given the recent advances of multimodal large language models (MLLMs), we raise the question: can MLLMs reform current trajectory data mining and solve the problem? Nevertheless, due to the modality gap of trajectory, how to generate task-independent multimodal trajectory representations and how to adapt flexibly to different tasks remain the foundational challenges. In this paper, we propose Traj-MLLM, which is the first general framework using MLLMs for trajectory data mining. By integrating multiview contexts, Traj-MLLM transforms raw trajectories into interleaved image-text sequences while preserving key spatial-temporal characteristics, and directly utilizes the reasoning ability of MLLMs for trajectory analysis. Additionally, a prompt optimization method is proposed to finalize data-invariant prompts for task adaptation. Extensive experiments on four publicly available datasets show that Traj-MLLM outperforms state-of-the-art baselines by 48.05%, 15.52%, 51.52%, 1.83% on travel time estimation, mobility prediction, anomaly detection and transportation mode identification, respectively. Traj-MLLM achieves these superior performances without requiring any training data or fine-tuning the MLLM backbones.

Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting have been proposed recently. However, we often observe inconsistent or seemingly contradictory performance findings across different studies. This hinders our understanding of the merits of different approaches and slows down progress. We address the need for means of assessing MTS forecasting proposals reliably and fairly, in turn enabling better exploitation of MTS as seen in different applications. Specifically, we first propose BasicTS+, a benchmark designed to enable fair, comprehensive, and reproducible comparison of MTS forecasting solutions. BasicTS+ establishes a unified training pipeline and reasonable settings, enabling an unbiased evaluation. Second, we identify the heterogeneity across different MTS as an important consideration and enable classification of MTS based on their temporal and spatial characteristics. Disregarding this heterogeneity is a prime reason for difficulties in selecting the most promising technical directions. Third, we apply BasicTS+ along with rich datasets to assess the capabilities of more than 30 MTS forecasting solutions. This provides readers with an overall picture of the cutting-edge research on MTS forecasting.

Trajectory clustering is an essential task in spatial data mining. To address this problem, many previous studies either extended traditional clustering algorithms with spatial features of trajectories or employed deep learning models for representation learning. However, one common drawback of existing solutions is that the final number of clusters needs to be specified as part of the input. In this paper, we proposed Tra-jDPM, an end-to-end framework for non-parametric trajectory clustering. We come up with two novel loss functions to pretrain a trajectory encoder so as to generate discriminative trajectory representation. Moreover, we employed the neural Dirichlet process mixture model to perform non-parametric clustering based on trajectory embeddings. In this process, the trajectory encoder can also be jointly optimized to improve the performance by a contrastive learning based strategy. We conduct an extensive set of evaluations on several public datasets. Experimental results show that our proposed framework can outperform state-of-the-art methods by a significant margin.

Trajectory anomaly detection, aiming to estimate the anomaly risk of trajectories given the Source-Destination (SD) pairs, has become a critical problem for many real-world applications. Existing solutions directly train a generative model for observed trajectories and calculate the conditional generative probability$P(T \vert C)$as the anomaly risk, where$T$and$C$represent the trajectory and SD pair respectively. However, we argue that the observed trajectories are confounded by road network preference which is a common cause of both SD distribution and trajectories. Existing methods ignore this issue limiting their generalization ability on out-of-distribution trajectories. In this paper, we define the debiased trajectory anomaly detection problem and propose a causal implicit generative model, namely CausalTAD, to solve it. CausalTAD adopts do-calculus to eliminate the confounding bias of road network preference and estimates$P(T\vert do(C))$as the anomaly criterion. Extensive experiments show that CausalTadcan not only achieve superior performance on trained trajectories but also generally improve the performance of out-of-distribution data, with improvements of 2.1% ~ 5.7% and 10.6% ~ 32.7% respectively.

Detecting anomaly edges for dynamic graphs aims to identify edges significantly deviating from the normal pattern and can be applied in various domains, such as cybersecurity, financial transactions and AIOps. With the evolving of time, the types of anomaly edges are emerging and the labeled anomaly samples are few for each type. Current methods are either designed to detect randomly inserted edges or require sufficient labeled data for model training, which harms their applicability for real-world applications. In this paper, we study this problem by cooperating with the rich knowledge encoded in large language models(LLMs) and propose a method, namely AnomalyLLM. To align the dynamic graph with LLMs, AnomalyLLM pretrains a dynamic-aware encoder to generate the representations of edges and reprograms the edges using the prototypes of word embeddings. Along with the encoder, we design an in-context learning framework that integrates the information of a few labeled samples to achieve few-shot anomaly detection. Experiments on four datasets reveal that AnomalyLlmcan not only significantly improve the performance of few-shot anomaly detection, but also achieve superior results on new anomalies without any update of model parameters.

In online advertising, marketing mix modeling (MMM) is employed to predict the gross merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget allocation of various advertising channels. Traditional MMM methods leveraging regression techniques can fail in handling the complexity of marketing. Although some efforts try to encode the causal structures for better prediction, they have the strict restriction that causal structures are prior-known and unchangeable. In this paper, we define a new causal MMM problem that automatically discovers the interpretable causal structures from data and yields better GMV predictions. To achieve causal MMM, two essential challenges should be addressed: (1) Causal Heterogeneity. The causal structures of different kinds of shops vary a lot. (2) Marketing Response Patterns. Various marketing response patterns i.e., carryover effect and shape effect, have been validated in practice. We argue that causal MMM needs dynamically discover specific causal structures for different shops and the predictions should comply with the prior known marketing response patterns. Thus, we propose CausalMMM that integrates Granger causality in a variational inference framework to measure the causal relationships between different channels and predict the GMV with the regularization of both temporal and saturation marketing response patterns. Extensive experiments show that CausalMMM can not only achieve superior performance of causal structure learning on synthetic datasets with improvements of 5.7%\sim 7.1%, but also enhance the GMV prediction results on a representative E-commerce platform.

Temporal data representing chronological observations of complex systems can be ubiquitously collected in smart industry, medicine, finance and etc. In the last decade, many tasks have been studied for mining temporal data and offered significant value for various applications. Among these tasks, causal discovery aims to understand the underlying generation mechanism of temporal data and has attracted much research attention. According to whether the data is calibrated, existing causal discovery approaches can be divided into two subtasks, i.e., multivariate time-series causal discovery, and event sequence causal discovery. Previous tutorials or surveys have primarily focused on causal discovery from time-series data and disregarded the second ones. In this tutorial, we elucidate the correlation between the two subtasks and provide a comprehensive review of the existing solutions. Moreover, we offer some potential applications and summarize new perspectives for discovering causal relations from temporal data. We hope the audiences can obtain a systematic overview of this topic and inspire some new ideas for their own research.

Nowadays safety has become one of the most critical factors for ride-hailing service. Ride-hailing platforms have conducted meticulous background checks for drivers to minimize the risk of abnormal trips, e.g. violence and sexual assault. However, current methods are labor-consuming and highly rely on the personal information of drivers, which may harm the fairness of the order dispatching system. In this paper, we utilize the trip trajectories as inputs and propose a dual variational auto-encoder(VAE) framework, namely TripSafe, to estimate the probability of abnormal safety incidents. Specifically, TripSafe models the moving behavior and route information, as two independent components and employs VAEs to pre-train generative models for normal trips. Then, a fusion network is adopted to fine-tune the whole model with a few labeled samples. In practice, TripSafe monitors the data update and calculate the anomaly score of partial-observed trips in real-time. Experiments on real ridehailing data show that TripSafe is superior to the state-of-the-art baselines with about 14.2%~28.9% improvements on F1 score.

Trajectory similarity computation as the fundamental problem for various downstream analytic tasks, such as trajectory classification and clustering, has been extensively studied in recent years. However, how to infer an accurate and robust similarity over two trajectories is difficult due to the some trajectory characteristics in practice, e.g. non-uniform sampling rate, nonmalignant fluctuation, and noise points, etc. To circumvent such challenges, we in this paper introduce the adversarial training idea into the trajectory representation learning for the first time to enhance the robustness and accuracy. Specifically, our proposed method AdvTraj2Vec has two novelties: i) it perturbs the weight parameters of embedding layers to learn a robust model to infer an accurate pairwise similarity over each two trajectories; and ii) it employs the GAN momentum to harness the perturbation extent to which an appropriate trajectory representation can be learned for the similarity computation. Extensive experiments using two real-world trajectory datasets Porto and Beijing validate our proposed AdvTraj2Vec on the robustness and accuracy aspects. The multi-facet results show that our AdvTraj2Vec significantly outperforms the stat-of-the-art methods in terms of different distortions, such as trajectory-point addition, deletion, disturbance, and outlier injection.

With the emerging of smartphones, mobile games have attracted billions of players and occupied most of the share for game companies. On the other hand, mobile game cheating, aiming to gain improper advantages by using programs that simulate the players' inputs, severely damages the game's fairness and harms the user experience. Therefore, detecting mobile game cheating is of great importance for mobile game companies. Many PC game-oriented cheating detection methods have been proposed in the past decades, however, they can not be directly adopted in mobile games due to the concern of privacy, power, and memory limitations of mobile devices. Even worse, in practice, the cheating programs are quickly updated, leading to the label scarcity for novel cheating patterns. To handle such issues, we in this paper introduce a mobile game cheating detection framework, namely FCDGame, to detect the cheats under the few-shot learning framework. FCDGame only consumes the screen sensor data, recording users' touch trajectories, which is less sensitive and more general for almost all mobile games. Moreover, a Hierarchical Trajectory Encoder and a Cross-pattern Meta Learner are designed in FCDGame to capture the intrinsic characters of mobile games and solve the label scarcity problem, respectively. Extensive experiments on two real online games show that FCDGame achieves almost 10% improvements in detection accuracy with only few fine-tuned samples.

Multi-touch attribution (MTA), aiming to estimate the contribution of each advertisement touchpoint in conversion journeys, is essential for budget allocation and automatically advertising. Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint by using the results counterfactual predictions. An assumption of these works is the conversion prediction model is unbiased. It can give accurate predictions on any randomly assigned journey, including both the factual and counterfactual ones. Nevertheless, this assumption does not always hold as the user preferences act as the common cause for both ad generation and user conversion, involving the confounding bias and leading to an out-of-distribution (OOD) problem in the counterfactual prediction. In this paper, we define the causal MTA task and propose CausalMTA to solve this problem. It systemically eliminates the confounding bias from both static and dynamic perspectives and learn an unbiased conversion prediction model using historical data. We also provide a theoretical analysis to prove the effectiveness of CausalMTA with sufficient ad journeys. Extensive experiments on both synthetic and real data in Alibaba advertising platform show that CausalMTA can not only achieve better prediction performance than the state-of-the-art method but also generate meaningful attribution credits across different advertising channels.

Computing trajectory similarities is a critical and fundamental task for various spatial-temporal applications, such as clustering, prediction, and anomaly detection. Traditional similarity metrics, i.e. DTW and Hausdorff, suffer from quadratic computation complexity, leading to their inability on large-scale data. To solve this problem, many trajectory representation learning techniques are proposed to approximate the metric space while reducing the complexity of similarity computation. Nevertheless, these works are designed based on RNN backend, resulting in a serious performance decline on long trajectories. In this paper, we propose a novel graph-based method, namely TrajGAT, to explicitly model the hierarchical spatial structure and improve the performance of long trajectory similarity computation. TrajGAT consists of two main modules, i.e. , graph construction and trajectory encoding. For graph construction, TrajGAT first employs PR quadtree to build the hierarchical structure of the whole spatial area, and then constructs a graph for each trajectory based on the original records and the leaf nodes of the quadtree. For trajectory encoding, we replace the self-attention in Transformer with graph attention and design an encoder to represent the generated graph trajectory. With these two modules, TrajGAT can capture the long-term dependencies of trajectories while reducing the GPU memory usage of Transformer. Our experiments on two real-life datasets show that TrajGAT not only improves the performance on long trajectories but also outperforms the state-of-the-art methods on mixture trajectories significantly.

This paper studies the task of detecting bots for online mobile games. Considering the fact of lacking labeled cheating samples and restricted available data in the real detection systems, we aim to study the finger operations captured by screen sensors to infer the potential bots in an unsupervised way. In detail, we introduce a Transformer-style detection model, namely FingFormer. It studies the finger operations in the format of graph structure in order to capture the spatial and temporal relatedness between the two hands’ operations. To optimize the model in an unsupervised way, we introduce two contrastive learning strategies to refine both finger moving patterns and players’ operation habits. We conduct extensive experiments under different experimental environments, including the synthetic dataset, the offline dataset, as well as the large-scale online data flow from three mobile games. The multi-facet experiments illustrate the proposed model is both effective and general to detect the bots for different mobile games.

Time series similarity computation is a fundamental primitive that underpins many time series data analysis tasks. However, many existing time series similarity measures have a high computation cost. While there has been much research effort for reducing the computational cost, such effort is usually specific to one similarity measure. We proposeNeuTS(Neural metric learning forTimeSeries) to accelerate time series similarity computation in a generic fashion.NeuTScomputes the similarity of a given time series pair in linear time and generic to handle any existing similarity measures.NeuTSsamples a number of seed time series from the given database, and then uses their pair-wise similarities as guidance to approximate the similarity function with a neural metric learning framework.NeuTSfeatures two novel modules to achieve accurate approximation of the similarity function: (1) a local attention memory module that augments existing recurrent neural networks for time series encoding; and (2) a distance-weighted ranking loss that effectively transcribes information from the seed-based guidance. With these two modules,NeuTScan yield high accuracies and fast convergence rates even if the training data is small. Our experiments with five real-life datasets and four similarity measures (Fréchet, Hausdorff, ERP and DTW) show thatNeuTSoutperforms baselines consistently and significantly. Specifically, it achieves over 80 percent accuracies in most settings, while obtaining 50x-1000x speedup over bruteforce methods and 3x-350x speedup over approximate algorithms for top-k similarity search.

In this paper, we propose a new task namely trajectory cross-modal retrieval which achieves the cross-modal search between coordinate trajectories and images containing trajectories. Nevertheless, trajectory cross-modal retrieval is rather challenging in learning the representations of each modality and reduce the cross-domain discrepancy caused by the inconsistent data distribution at the same time. we proposes a cross-modal retrieval model TrajCross based on multi-level representation for trajectory cross-modal retrieval. Specifically, TrajCross extracts the location features and the shape information respectively for the represention of multi-modal data. we adopt a contrastive learning method to achieve semantic preservation among similar multi-modal data. Extensive experiments show that TrajCross significantly outperforms state-of-the-art cross-modal retrieval methods.

Trajectory similarity computation is an essential operation in many applications of spatial data analysis. In this paper, we study the problem of trajectory similarity computation over spatial network, where the real distances between objects are reflected by the network distance. Unlike previous studies which learn the representation of trajectories in Euclidean space, it requires to capture not only the sequence information of the trajectory but also the structure of spatial network. To this end, we propose GTS, a brand new framework that can jointly learn both factors so as to accurately compute the similarity. It first learns the representation of each point-of-interest (POI) in the road network along with the trajectory information. This is realized by incorporating the distances between POIs and trajectory in the random walk over the spatial network as well as the loss function. Then the trajectory representation is learned by a Graph Neural Network model to identify neighboring POIs within the same trajectory, together with an LSTM model to capture the sequence information in the trajectory. We conduct comprehensive evaluation on several real world datasets. The experimental results demonstrate that our model substantially outperforms all existing approaches.

Recommending citywide POIs where users would visit in the next future benefits many location-based businesses and individuals. To train a decent recommendation model, adequate historical data is usually a prerequisite. However, historical check-ins are usually distributed unevenly, which leads to many cities suffering from data scarcity. To make matters worse, transferring knowledge from data sufficient cities is challenging due to the varying distribution of POIs and city structures. Most of existing next POI recommendation methods assume that the training data is adequate and can not solve these problems. In this paper, we propose a novel meta-learning enhanced neural ordinary differential equation (ODE) method, namely METAODE, which models city-invariant information and city-specified information separately to achieve accurate citywide next POI recommendation. For transferring knowledge from data sufficient cities, METAODE learns city-invariant information including the representation of POIs categories and user groups to extract user preference. Basing on that, METAODE employs a GRU-ODE-Bayes model for city-specified information modeling. It can not only capture the sequential relationships within the historical check-ins but also model the irregular-sampled timestamp in the continuous timeline. Moreover, METAODE leverages meta-learning mechanism to optimize the parameters on various data sufficient cities and train a well-generalized initialization, which can be effectively adapted to data insufficient cities to enhance recommendation performance. Extensive experiments on real-world datasets demonstrate the effectiveness of METAODE. Comparing with the state-of-the-art baselines, METAODE achieves 6.21% and 14.77% improvements on HR and NDCG, respectively.

To ensure the security and stabilization of the communication networks, anomaly detection is the first line of defense. However, their learning process suffers two major issues: (1) inadequate labels : there are many different kinds of attacks but rare abnormal nodes in mt of these atstacks; and (2) inaccurate labels : considering the heavy network flows and new emerging attacks, providing accurate labels for all nodes is very expensive. The inadequate and inaccurate label problem challenges many existing methods because the majority normal nodes result in a biased classifier while the noisy labels will further degrade the performance of the classifier. To tackle these issues, we propose SemiADC, a Semi -supervised A nomaly D etection framework for dynamic C ommunication networks. SemiADC first approximately learns the feature distribution of normal nodes with regularization from abnormal ones. It then cleans the datasets and extracts the nodes sasainaccurate labels by the learned feature distribution and structure-based temporal correlations. These self-learning processes run iteratively with mutual promotion, and finally help increase the accuracy of anomaly detection. Experimental evaluations on real-world datasets demonstrate the effectiveness of our SemiADC, which performs substantially better than the state-of-art anomaly detection approaches without the demand of adequate and accurate supervision.

Trajectory similarity computation is a fundamental problem for various applications in trajectory data analysis. However, the high computation cost of existing trajectory similarity measures has become the key bottleneck for trajectory analysis at scale. While there have been many research efforts for reducing the complexity, they are specific to one similarity measure and often yield limited speedups. We propose NeuTraj to accelerate trajectory similarity computation. NeuTraj is generic to accommodate any existing trajectory measure and fast to compute the similarity of a given trajectory pair in linear time. Furthermore, NeuTraj is elastic to collaborate with all spatial-based trajectory indexing methods to reduce the search space. NeuTraj samples a number of seed trajectories from the given database, and then uses their pair-wise similarities as guidance to approximate the similarity function with a neural metric learning framework. NeuTraj features two novel modules to achieve accurate approximation of the similarity function: (1) a spatial attention memory module that augments existing recurrent neural networks for trajectory encoding; and (2) a distance-weighted ranking loss that effectively transcribes information from the seed-based guidance. With these two modules, NeuTraj can yield high accuracies and fast convergence rates even if the training data is small. Our experiments on two real-life datasets show that NeuTraj achieves over 80% accuracy on Fre chet, Hausdorff, ERP and DTW measures, which outperforms state-of-the-art baselines consistently and significantly. It obtains 50x-1000x speedup over bruteforce methods and 3x-500x speedup over existing approximate algorithms, while yielding more accurate approximations of the similarity functions.

Recently, data mining through analyzing the complex structure and diverse relationships on multi-network has attracted much attention in both academia and industry. One crucial prerequisite for this kind of multi-network mining is to map the nodes across different networks, i.e., so-called network alignment. In this paper, we propose a cross-network embedding method CrossMNA for multi-network alignment problem through investigating structural information only. Unlike previous methods focusing on pair-wise learning and holding the topology consistent assumption, our proposed CrossMNA considers the multi-network scenarios which involve at least two types of networks with diverse network structures. CrossMNA leverages the cross-network information to refine two types of node embedding vectors, i.e., inter-vector for network alignment and intra-vector for other downstream network analysis tasks. Finally, we verify the effectiveness and efficiency of our proposed method using several real-world datasets. The extensive experiments show that our CrossMNA can significantly outperform the existing baseline methods on multi-network alignment task, and also achieve better performance for link prediction task with less memory usage.

Predicting the next location a user tends to visit is an important task for applications like location-based advertising, traffic planning, and tour recommendation. We consider the next location prediction problem for semantic trajectory data, wherein each GPS record is attached with a text message that describes the user's activity. In semantic trajectories, the confluence of spatiotemporal transitions and textual messages indicates user intents at a fine granularity and has great potential in improving location prediction accuracies. Nevertheless, existing methods designed for GPS trajectories fall short in capturing latent user intents for such semantics-enriched trajectory data. We propose a method named semantics-enriched recurrent model (SERM). SERM jointly learns the embeddings of multiple factors (user, location, time, keyword) and the transition parameters of a recurrent neural network in a unified framework. Therefore, it effectively captures semantics-aware spatiotemporal transition regularities to improve location prediction accuracies. Our experiments on two real-life semantic trajectory datasets show that SERM achieves significant improvements over state-of-the-art methods.