Trajectory representation learning (TRL) maps trajectories to vectors that can be used for many downstream tasks. Existing TRL methods use either grid trajectories, capturing movement in free space, or road trajectories, capturing movement in a road network, as input. We observe that the two types of trajectories are complementary, providing either region and location information or providing road structure and movement regularity. Therefore, we propose a novel multimodal TRL method, dubbed GREEN, to jointly utilize Grid and Road trajectory Expressions for Effective representatioN learning. In particular, we transform raw GPS trajectories into both grid and road trajectories and tailor two encoders to capture their respective information. To align the two encoders such that they complement each other, we adopt a contrastive loss to encourage them to produce similar embeddings for the same raw trajectory and design a mask language model (MLM) loss to use grid trajectories to help reconstruct masked road trajectories. To learn the final trajectory representation, a dual-modal interactor is used to fuse the outputs of the two encoders via cross-attention. We compare GREEN with 7 state-of-the-art TRL methods for 3 downstream tasks, finding that GREEN consistently outperforms all baselines and improves the accuracy of the best-performing baseline by an average of 15.99%. Code and data are available at https://github.com/slzhou-xy/GREEN.

Road trajectory generation creates synthetic yet realistic trajectories to tackle data collection costs and privacy concerns. Existing methods generate a trajectory either segment-by-segment using sequence models or holistically in one step using diffusion models. Sequence-based models have good regularity and consistency (i.e., resemble the input trajectories) but lack diversity, while diffusion-based models enhance diversity but sacrifice regularity and consistency. To combine the merits of existing methods, we propose Seed, by bridging sequence and diffusion models for trajectory generation. In particular, Seed adopts a conditional diffusion structure, where a Transformer models the movement of each trajectory along the road segments, and conditioned on the Transformer's output, a diffusion model recovers the next road segment from random noise. The rationale is that the Transformer captures sequential patterns for regularity and consistency, while the diffusion model introduces diversity by recovering from noise. We use a trajectory reconstruction task to train Seed, and design a curriculum learning strategy to accelerate convergence. We compare Seed with 8 state-of-the-art trajectory generation methods on 3 datasets, and the results show that Seed improves the best-performing baseline by over 50%.

Next point-of-interest (POI) recommendationpredicts user’s next movement and facilitates location-based applications such as destination suggestion and travel planning. State-of-the-art (SOTA) methods learn an adaptive graph from user trajectories and compute POI representations using graph neural networks (GNNs). However, a single graph cannot capture thediverse dependenciesamong the POIs (e.g., geographical proximity and transition frequency). To tackle this limitation, we propose theAdaptiveGraphContrastiveLearning(AGCL) framework. AGCL constructs multiple adaptive graphs, each modeling a kind of POI dependency and producing one POI representation; and the POI representations from different graphs are merged into amulti-facet representationthat encodes comprehensive information. To train the POI representations, we tailor agraph-based contrastive learning, which encourages the representations of similar POIs to align and dissimilar POIs to differentiate. Moreover, to learn the sequential regularities of user trajectories, we design an attention mechanism to integrate spatial-temporal information into the POI representations. An explicitspatial-temporal biasis also employed to adjust the predictions for enhanced accuracy. We compare AGCL with 10 state-of-the-art baselines on 3 datasets. The results show that AGCL outperforms all baselines and achieves an improvement of 10.14% over the best performing baseline in average accuracy.

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.

Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use of graph neural networks to capture latent correlations between time series holds a potential for enhanced forecasting. However, most existing methods rely on predefined or self-learning graphs, which are either static or unintentionally dynamic, and thus cannot model the time-varying correlations that exhibit trends and periodicities caused by the regularity of the underlying processes in CPS. To tackle such limitation, we propose Time-aware Graph Structure Learning (TagSL), which extracts time-aware correlations among time series by measuring the interaction of node and time representations in high-dimensional spaces. Notably, we introduce time discrepancy learning that utilizes contrastive learning with distance-based regularization terms to constrain learned spatial correlations to a trend sequence. Additionally, we propose a periodic discriminant function to enable the capture of periodic changes from the state of nodes. Next, we present a Graph Convolution-based Gated Recurrent Unit (GCGRU) that jointly captures spatial and temporal dependencies while learning time-aware and node-specific patterns. Finally, we introduce a unified framework named Time-aware Graph Convolutional Recurrent Network (TGCRN), combining TagSL, and GCGRU in an encoder-decoder architecture for multi-step spatiotemporal forecasting. We report on experiments with TGCRN and popular existing approaches on five real-world datasets, thus providing evidence that TGCRN is capable of advancing the state-of-the-art. We also cover a detailed ablation study and visualization analysis, offering detailed insight into the effectiveness of time-aware structure learning.

Due to the global trend towards urbanization, people increasingly move to and live in cities that then continue to grow. Traffic forecasting plays an important role in the intelligent transportation systems of cities as well as in spatio-temporal data mining. State-of-the-art forecasting is achieved by deep-learning approaches due to their ability to contend with complex spatio-temporal dynamics. However, existing methods assume the input is fixed-topology road networks and static traffic time series. These assumptions fail to align with urbanization, where time series are collected continuously and road networks evolve over time. In such settings, deep-learning models require frequent re-initialization and re-training, imposing high computational costs. To enable much more efficient training without jeopardizing model accuracy, we propose the Topological Evolution-aware Framework (TEAM) for traffic forecasting that incorporates convolution and attention. This combination of mechanisms enables better adaptation to newly collected time series while being able to maintain learned knowledge from old time series. TEAM features a continual learning module based on the Wasserstein metric that acts as a buffer that can identify the most stable and the most changing network nodes. Then, only data related to stable nodes is employed for re-training when consolidating a model. Further, only data of new nodes and their adjacent nodes as well as data pertaining to changing nodes are used to re-train the model. Empirical studies with two real-world traffic datasets offer evidence that TEAM is capable of much lower re-training costs than existing methods are, without jeopardizing forecasting accuracy.

Empathy is one of the fundamental abilities of dialog systems. In order to build more intelligent dialogue systems, it’s important to learn how to demonstrate empathy toward others. Existing studies focus on identifying and leveraging the user’s coarse emotion to generate empathetic responses. However, human emotion and dialog act (e.g., intent) evolve as the talk goes along in an empathetic dialogue. This leads to the generated responses with very different intents from the human responses. As a result, empathy failure is ultimately caused. Therefore, using fine-grained emotion and intent sequential data on conversational emotions and dialog act is crucial for empathetic response generation. On the other hand, existing empathy models overvalue the empathy of responses while ignoring contextual relevance, which results in repetitive model-generated responses. To address these issues, we propose a Multi-Factor sequence Fusion framework (EmpMFF) based on conditional variational autoencoder. To generate empathetic responses, the proposed EmpMFF encodes a combination of contextual, emotion, and intent information into a continuous latent variable, which is then fed into the decoder. Experiments on the EmpatheticDialogues benchmark dataset demonstrate that EmpMFF exhibits exceptional performance in both automatic and human evaluations.

Abstract Re-ranking is to refine the candidate ranking list of recommended items, such that the re-ranked list attracts users to purchase or click more items than the candidate one without re-ranking. Items in the candidate list are often ranked by their relevance to users’ interests. It is thus important to exploit the mutual influence between items in the re-ranking process. Existing re-ranking models focus on only the pairwise influence between two items, and have limited capability to exploit the local mutual influence in a group of items. Users often show successive interests on a group of relevant items, e.g., mobile phone, phone covers, wireless headset, namely scene. We propose a novel re-ranking model that jointly exploits the local mutual influence in scenes and the global mutual influence between different scenes. Scene representations are learned by GNN and multi-head attention, where GNN aims to learn local mutual influence while multi-head attention is to learn global mutual influence. To study the interaction between users and scenes, matrix factorization on users is utilized to obtain the user preference, which can be further applied to scenes to compute the scene scores. The final re-ranking list is generated by sorting the predicted scores of all scenes. To further mine user history information and item related user information, we also develop the extension pretraining module which relies on mask mechanism to support users and items high-quality embedding generation. We conduct a comprehensive evaluation on several real-world datasets. The experimental results demonstrate that our model substantially outperforms existing approaches.

Multiple time series forecasting plays an essential role in many applications. Solutions based on graph neural network (GNN) that deliver state-of-the-art forecasting performance use the relation graph which can capture historical correlations among time series. However, in real world, it is common that correlations among time series evolve across time, resulting in dynamic relation graph, where the future correlations may be different from those in history. To address this problem, we propose multiple time series forecasting with dynamic graph modeling (MTSF-DG) that is able to learn historical relation graphs and predicting future relation graphs to capture the dynamic correlations. We also propose a causal GNN to extract features from both kinds of relation graphs efficiently. Then we propose a reasoning network to explicitly learn the variant influence from historical timestamps to future timestamps for final forecasting. Extensive experiments on six benchmark datasets show that MTSF-DG consistently outperforms state-of-the-art baselines, and justify our design with dynamic relation graph modeling.

Recent neural architectures in sequence labeling have yielded state-of-the-art performance on single domain data such as newswires. However, they still suffer from (i) requiring massive amounts of training data to avoid overfitting; (ii) huge performance degradation when there is a domain shift in the data distribution between training and testing. In this paper, we investigate the problem of domain adaptation for sequence labeling under homogeneous and heterogeneous settings. We propose MetaSeq, a novel meta-learning approach for domain adaptation in sequence labeling. Specifically, MetaSeq incorporates meta-learning and adversarial training strategies to encourage robust, general and transferable representations for sequence labeling. The key advantage of MetaSeq is that it is capable of adapting to new unseen domains with a small amount of annotated data from those domains. We extensively evaluate MetaSeq on named entity recognition, part-of-speech tagging and slot filling tasks under homogeneous and heterogeneous settings. The experimental results show that MetaSeq achieves state-of-the-art performance against eight baselines. Impressively, MetaSeq surpasses the in-domain performance using only 16.17% and 7% of target domain data on average for homogeneous settings, and 34.76%, 24%, 22.5% of target domain data on average for heterogeneous settings.

Urban traffic flow prediction plays a crucial role in smart city management. Since the flow volume of one road (treated as a node in a traffic network) in future time units (after time t) depends on the historical volume (before and including time t) of this road (node) itself and its neighboring roads (nodes), the traffic flow prediction problem has recently been studied by utilizing a spatial-temporal adjacency matrix (AM) of traffic nodes constructed from the historical traffic and node connections. The construction of AM is often based on statistical traffic information before t, instead of using the volume at individual time unit level. In addition, the spatial and temporal relations between traffic nodes are manually fused in AM, rather than in a trainable fusion. In order to conquer these issues, we propose a trainable context enhanced similarity graph, which fuses the unit-level similarity of traffic time series and multiple inter-node contextual relations through a learnable embedding model. In addition, a Quotient Neural Network is proposed to perceive the explicit relation among short-memory flow values and facilitate the forecasting. Based on the two modules, we propose a novel Quotient Graph Neural Network (QuoGNN). Experiments on four real-world benchmark datasets demonstrate the superior performance of our proposed model over the state-of-the-art baselines on multiple evaluation metrics. The implementation of the model and datasets are available1.

Retrosynthetic planning, which aims to find a reaction pathway to synthesize a target molecule, plays an important role in chemistry and drug discovery. This task is usually modeled as a search problem. Recently, data-driven methods have attracted many research interests and shown promising results for retrosynthetic planning. We observe that the same intermediate molecules are visited many times in the searching process, and they are usually independently treated in previous tree-based methods (e.g., AND-OR tree search, Monte Carlo tree search). Such redundancies make the search process inefficient. We propose a graph-based search policy that eliminates the redundant explorations of any intermediate molecules. As searching over a graph is more complicated than over a tree, we further adopt a graph neural network to guide the search over graphs. Meanwhile, our method can search a batch of targets together in the graph and remove the inter-target duplication in the tree-based search methods. Experimental results on two datasets demonstrate the effectiveness of our method. Especially on the widely used USPTO benchmark, we improve the search success rate to 99.47%, advancing previous state-of-the-art performance for 2.6 points.

Spatial temporal forecasting plays an important role in improving the quality and performance of Intelligent Transportation Systems. This task is rather challenging due to the complicated and long-range spatial temporal dependencies in traffic network. Existing studies typically employ different deep neural networks to learn the spatial and temporal representations so as to capture the complex and dynamic dependencies. In this paper, we argue that it is insufficient to capture the long-range spatial dependencies from the implicit representations learned by temporal extracting modules. To address this problem, we propose Multi-Step Dependency Relation (MSDR), a brand new variant of recurrent neural network. Instead of only looking at the hidden state from only one latest time step, MSDR explicitly takes those of multiple historical time steps as the input of each time unit. We also develop two strategies to incur the spatial information into the dependency relation embedding between multiple historical time steps and the current one in MSDR. On the basis of it, we propose the Graph-based MSDR (GMSDR) framework to support general spatial temporal forecasting applications by seamlessly integrating graph-based neural networks with MSDR. We evaluate our proposed approach on several popular datasets. The results show that the proposed GMSDR framework outperforms state-of-the-art methods by an obvious margin.

Next Point-of Interest (POI) recommendation plays an important role in location-based applications, which aims to recommend the next POIs to users that they are most likely to visit based on their historical trajectories. Existing methods usually use rich side information, or customized POI graphs to capture the sequential patterns among POIs. However, the graphs only focus on connectivity between POIs. Few studies propose to explicitly learn a weighted POI graph, which could reflect the transition patterns among POIs and show the importance of its different neighbors for each POI. In addition, these approaches simply utilize the user characteristics for personalized POI recommendation without sufficient consideration. To this end, we construct a novel User-POI Knowledge Graph with strong representation ability, called Spatial-Temporal Knowledge Graph (STKG). STKG is used to learn the representations of each node (i.e., user, POI) and each edge. Then, we design a similarity function to construct our POI transition graph based on the learned representations. To incorporate the learned graph into sequential model, we propose a novel network Graph-Flashback for recommendation. Graph-Flashback applies a simplified Graph Convolution Network (GCN) on the POI transition graph to enrich the representation of each POI. Further, we define a similarity function to consider both spatiotemporal information and user preference in modelling sequential regularity. Experimental results on two real-world datasets show that our proposed method achieves the state-of-the-art performance and significantly outperforms all existing solutions.

The task of point of interest (POI) recommendation aims to recommend unvisited places to users based on their check-in history. A major challenge in POI recommendation is data sparsity, because a user typically visits only a very small number of POIs among all available POIs. In this paper, we propose AUC-MF to address the POI recommendation problem by maximizing Area Under the ROC curve (AUC). AUC has been widely used for measuring classification performance with imbalanced data distributions. To optimize AUC, we transform the recommendation task to a classification problem, where the visited locations are positive examples and the unvisited are negative ones. We define a new lambda for AUC to utilize the LambdaMF model, which combines the lambda-based method and matrix factorization model in collaborative filtering. Many studies have shown that geographic information plays an important role in POI recommendation. In this study, we focus on two levels geographic information: local similarity and global similarity. We further show that AUC-MF can be easily extended to incorporate geographical contextual information for POI recommendation. Specifically, we propose two novel methods to incorporate geographical information in AUC-MF. Different from most existing models where the contextual information are incorporated into the objective function, the incorporation of contextual information in AUC-MF is a refinement of the model and a sampling strategy. The sampling strategy could speedup convergence and the refining of recommendations is independent of training of the model. This mechanism also enables AUC-MF to be able produce recommendations refined towards different contextual information, with minimum computational cost. Experiments on two datasets show that the proposed AUC-MF outperforms state-of-the-art methods significantly in terms of recommendation accuracy.

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.

The task of point of interest (POI) recommendation aims to recommend unvisited places to users based on their check-in history. A major challenge in POI recommendation is data sparsity, because a user typically visits only a very small number of POIs among all available POIs. In this paper, we propose AUC-MF to address the POI recommendation problem by maximizing Area Under the ROC curve (AUC). AUC has been widely used for measuring classification performance with imbalanced data distributions. To optimize AUC, we transform the recommendation task to a classification problem, where the visited locations are positive examples and the unvisited are negative ones. We define a new lambda for AUC to utilize the LambdaMF model, which combines the lambda-based method and matrix factorization model in collaborative filtering. Experiments on two datasets show that the proposed AUC-MF outperforms state-of-the-art methods significantly in terms of recommendation accuracy.

Discovering disease-gene association is a fundamental and critical biomedical task, which assists biologists and physicians to discover pathogenic mechanism of syndromes. With various clinical biomarkers measuring the similarities among genes and disease phenotypes, network-based semi-supervised learning (NSSL) has been commonly utilized by these studies to address this class-imbalanced large-scale data issue. However, most existing NSSL approaches are based on linear models and suffer from two major limitations: 1) They implicitly consider a local-structure representation for each candidate; 2) They are unable to capture nonlinear associations between diseases and genes. In this paper, we propose a new framework for disease-gene association task by combining Graph Convolutional Network (GCN) and matrix factorization, named GCN-MF. With the help of GCN, we could capture non-linear interactions and exploit measured similarities. Moreover, we define a margin control loss function to reduce the effect of sparsity. Empirical results demonstrate that the proposed deep learning algorithm outperforms all other state-of-the-art methods on most of metrics.