Multi-modal Entity Alignment (MMEA) aims to identify equivalent entities across diverse knowledge graphs by leveraging structural, attribute, and visual information. However, real-world datasets frequently suffer from missing modalities, necessitating feature imputation. A critical yet underexplored issue is that not all imputed modalities are inherently trustworthy. Ignoring the aleatoric uncertainty of such modalities introduces severe noise which propagates through the fusion process and degrades alignment performance. To address this challenge, we propose a novel MMEA framework, namely SURE, to Suppress Uncertainty for tRustworthy Entity alignment. Specifically, SURE introduces an uncertainty-aware variational imputation module to estimate the aleatoric uncertainty of generated features. Crucially, rather than using these imputation features blindly, SURE leverages the estimated uncertainty to suppress noise propagation via a confidence-gated multi-modal fusion and an adaptive contrastive learning objective. Extensive experiments on DBP15K datasets demonstrate that SURE significantly outperforms state-of-the-art baselines, exhibiting exceptional robustness particularly in scenarios with high modality missing rates.

In recent years, large language models (LLMs) have emerged as powerful tools for link prediction in knowledge graphs (KGs) due to their strong capabilities in understanding and generation. However, many LLM-based methods still heavily rely on textual descriptions of KGs, limiting their ability to capture structural information and to model complex relational patterns. Although some methods integrate structural embeddings into LLMs, their ability to harness the complementary strengths of both modalities and dynamically prioritize candidate entities based on query context remains limited. In this paper, we propose ST-KGLP, a novel framework that improves link prediction by aligning structural knowledge with textual knowledge and employing query-aware adaptive weighting for candidate selection. Specifically, our proposed ST-KGLP employs a knowledge aligner to bridge the information gap between structural and textual knowledge, and then utilizes a query-aware adaptive weighting strategy that dynamically computes attention weights between query representations and candidate entities, enabling contextually relevant candidate re-ranking for more accurate prediction. Extensive experiments on various datasets show that our ST-KGLP outperforms state-of-the-art approaches, achieving average improvements of 3.81%, 11.52%, 2.22%, and 1.55% across four evaluation metrics. Our code and datasets are available at https://github.com/shijielaw/ST-KGLP.

To achieve multi-hop question answering over knowledge graphs (KGQA), many studies have explored converting retrieved subgraphs into textual form and feeding them into large language models (LLMs) to leverage their reasoning capabilities. However, due to the linear and discrete nature of text sequences, model performance may degrade when handling complex questions. To this end, we propose a novel structure-text knowledge synergistic method, BrikQA, which bridges the knowledge gap between knowledge graphs (KGs) and LLMs for multi-hop KGQA. LLMs and KGs complement each other by leveraging explicit topological patterns and implicit knowledge mining to enhance knowledge understanding and address sparsity issues. Experimental results on various datasets demonstrate that BrikQA outperforms state-of-the-art baselines. Our source code is available at https://github.com/shijielaw/BrikQA.

Recently, deep reinforcement learning (DRL) has been employed for intelligent traffic‐light control and demonstrated promising results. However, state‐of‐the‐art DRL‐based systems still rely on discrete decision‐making, which can lead to unsafe driving practices. Additionally, existing feature representations of the environment often fail to capture the complex dynamics of traffic flows, resulting in imprecise predictions of traffic conditions. To address these issues, we propose a novel DRL framework based on the multiagent deep deterministic policy gradient algorithm. Our method offers several key innovations: it suggests employing a transitional phase before changing the current phase for safer traffic management, integrates local road network topology into feature representation to enhance the accuracy of traffic flow predictions, and uses two‐layer regional features to improve coordination among agents within the region. Our extensive evaluations using simulation of urban mobility, a widely used multimodal traffic simulation package, demonstrated that the proposed method outperformed previous methods and reduced the number of emergency stops, queue lengths, and waiting times.

Recently deep reinforcement learning (DRL) has been used for intelligent traffic light control. Unfortunately, we find that state-of-the-art on DRL-based intelligent traffic light essentially adopts discrete decision making and would suffer from the issue of unsafe driving. Moreover, existing feature representation of environment may not capture dynamics of traffic flow and thus cannot precisely predict future traffic flows. To overcome these issues, in this paper, we propose a DDPG-based DRL framework to learn a continuous time duration of traffic signal phases by introducing 1) a transit phase before the change of current phase for better safety, and 2) vehicle moving speed into feature representation for more precise estimation of traffic flow in next phase. Our preliminary evaluation on a well-known simulator SUMO indicates that our work significantly outperforms a recent work by much smaller number of emergency stops, queue length and waiting time.

Multivariate time series forecasting has wide applications such as traffic flow prediction, supermarket commodity demand forecasting and etc. In literature, Due to the complex temporal patterns and inter-dependencies among multivariate time series, a large number of forecasting models have been developed. However, one question still remains unclear: how these models perform on a certain forecasting task, and there is lack of comprehensive performance comparison of these models on different tasks. To this end, in this paper, we conduct a systematic evaluation of eight representative forecasting models over eight multivariate time series datasets, and have the following findings: 1) When the datasets exhibit strong periodic patterns, deep learning models perform best. Otherwise on the datasets in a non-periodic manner, the statistical models such as ARIMA perform best. 2) For the long term prediction involving a high horizon value, the direct prediction strategy could lead to lower errors than the recursive one, but at the cost of higher training time. 3) For the multivariate time series explicitly involving graphic inter-dependencies among the multivariates, e.g., the road network topology in the spatio-temporal time series of traffic volumes in multiple routes, the Graph Convolution Network can incorporate the graphic inter-dependencies into their forecasting models for smaller prediction errors.

Accurate prediction of traffic congestion at the granularity of road segment is important for planning travel routes and optimizing traffic control in urban areas. Previous works often calculated only the average congestion levels of a large region covering many road segments and did not take into account spatial correlation between road segments, resulting in inaccurate and coarse-grained prediction. To overcome these issues, we propose in this paper CPM-ConvLSTM, a spatiotemporal model for short-term prediction of congestion level in each road segment. Our model is built on a spatial matrix which incorporates both the congestion propagation pattern and the spatial correlation between road segments. The preliminary experiments on the traffic data set collected from Helsinki, Finland prove that CPM-ConvLSTM greatly outperforms 6 counterparts in terms of prediction accuracy.

The volume of trajectory data has become tremendously large in recent years. How to efficiently maintain and compute such trajectory data becomes a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road networks. By treating the mined patterns as dictionary items, we have the chance to encode a long trajectory by shorter paths, thus leading to smaller space cost. Meanwhile, we design an error-bounded temporal compression on top of the identified spatial patterns for much low space cost. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of both space saving and runtime of trajectory compression.

The development of location-based applications raises a new challenge to manage and visualize large amounts of geo-tags presented on a web map. The visualization of the geo-tags often leads to a clutter problem, especially in web-mapping systems. We present a new clustering method to reduce the amount of visual clutter. A split smart swap strategy, which has the advantage that it can be applied to a certain data only once at all map scales, is employed in the method. We compare the proposed method to several other methods. Taking the advantage of the one-time running offline, the proposed method is more applicable for the clutter problem.

Clustering algorithm and cluster validity are two highly correlated parts in cluster analysis. In this paper, a novel idea for cluster validity and a clustering algorithm based on the validity index are introduced. A Centroid Ratio is firstly introduced to compare two clustering results. This centroid ratio is then used in prototype-based clustering by introducing a Pairwise Random Swap clustering algorithm to avoid the local optimum problem of k -means. The swap strategy in the algorithm alternates between simple perturbation to the solution and convergence toward the nearest optimum by k -means. The centroid ratio is shown to be highly correlated to the mean square error (MSE) and other external indices. Moreover, it is fast and simple to calculate. An empirical study of several different datasets indicates that the proposed algorithm works more efficiently than Random Swap, Deterministic Random Swap, Repeated k-means or k-means++. The algorithm is successfully applied to document clustering and color image quantization as well.