The rapid spread of diverse information on online social platforms has prompted both academia and industry to realize the importance of predicting content popularity, which could benefit a wide range of applications, such as recommendation systems and strategic decision-making. Recent works mainly focused on extracting spatiotemporal patterns inherent in the information diffusion process within a given observation period so as to predict its popularity over a future period of time. However, these works often overlook the future popularity trend, as future popularity could either increase exponentially or stagnate, introducing uncertainties to the prediction performance. Additionally, how to transfer the preceding-term dynamics learned from the observed diffusion process into future-term trends remains an unexplored challenge. Against this background, we propose CasFT, which leverages observed information Cascades and dynamic cues extracted via neural ODEs as conditions to guide the generation of Future popularity-increasing Trends through a diffusion model. These generated trends are then combined with the spatiotemporal patterns in the observed information cascade to make the final popularity prediction. Extensive experiments conducted on three real-world datasets demonstrate that CasFT significantly improves the prediction accuracy compared to state-of-the-art approaches.

With the ubiquity of hyper-relational facts in modern Knowledge Graphs (KGs), existing link prediction techniques mostly focus on learning the sophisticated relationships among multiple entities and relations contained in a fact, while ignoring the multimodal information, which often provides additional clues to boost link prediction performance.Nevertheless, traditional multimodal fusion approaches, which are mainly designed for triple facts under either entity-centric or relation-guided fusion schemes, fail to integrate the multimodal information with the rich context of the hyperrelational fact consisting of multiple entities and relations.Against this background, we propose HyperFM, a Hyper-relational Factcentric Multimodal Fusion technique.It effectively captures the intricate interactions between different data modalities while accommodating the hyper-relational structure of the KG in a fact-centric manner via a customized Hypergraph Transformer.We evaluate Hy-perFM against a sizeable collection of baselines in link prediction tasks on two real-world KG datasets.The results show that HyperFM consistently achieves the best performance, yielding an average improvement of 6.0-6.8% over the best-performing baselines on the two datasets.Moreover, a series of ablation studies systematically validate our fact-centric fusion scheme.

Synthetic human trajectory data becoming increasingly prominent in various applications, including urban planning, traffic control, and crowd monitoring. Recent neural generative models for human trajectory data mostly follow an unconditional generative paradigm that relies on a pure data-driven imitative learning scheme, without considering the rich context of human mobility (e.g., social events or weather conditions) which may significantly impact the underlying human mobility patterns. Against this background, we propose Marionette, a Manipulatable generative model for human trajectory data with fine-grained conditions. Specifically, Marionette integrates both global and partial mobility-related contexts and extracts both sequence-level and event-level conditions. Afterward, it designs fine-grained and cascading conditioning mechanisms for modeling the temporal and spatial dynamics based on diffusion-alike Temporal Point Processes (TPPs) and discrete diffusion models, respectively, offering fine-grained controllable generative modeling of human trajectory data with both global and partial mobility-related contexts. We conduct a thorough evaluation on two real-world human trajectory datasets against a sizeable collection of baselines. Results show that our Marionette consistently outperforms the best baselines by 13.96-54.13% on statistical and distributional similarity metrics and by 9.36-40.63% in task-based data utility evaluation. Ablation studies verify our key design choices. Case studies also demonstrate the manipulability of Marionette in generating data in previously unseen scenarios.

Human trajectory data, which plays a crucial role in various applications such as crowd management and epidemic prevention, is challenging to obtain due to practical constraints and privacy concerns. In this context, synthetic human trajectory data is generated to simulate as close as possible to real-world human trajectories, often under summary statistics and distributional similarities. However, these similarities oversimplify complex human mobility patterns (a.k.a. ''Datasaurus''), resulting in intrinsic biases in both generative model design and benchmarks of the generated trajectories. Against this background, we propose MIRAGE, a huMan-Imitative tRAjectory GenErative model designed as a neural Temporal Point Process integrating an Exploration and Preferential Return model. It imitates the human decision-making process in trajectory generation, rather than fitting any specific statistical distributions as traditional methods do, thus avoiding the Datasaurus issue. We also propose a comprehensive task-based evaluation protocol beyond Datasaurus to systematically benchmark trajectory generative models on four typical downstream tasks, integrating multiple techniques and evaluation metrics for each task, to assess the ultimate utility of the generated trajectories. We conduct a thorough evaluation of MIRAGE on three real-world user trajectory datasets against a sizeable collection of baselines. Results show that compared to the best baselines, MIRAGE-generated trajectory data not only achieves the best statistical and distributional similarities with 59.0-67.7% improvement, but also yields the best performance in the task-based evaluation with 10.9-33.4% improvement. A series of ablation studies also validate the key design choices of MIRAGE.

Information popularity prediction is important yet challenging in various domains, including viral marketing and news recommendations. The key to accurately predicting information popularity lies in subtly modeling the underlying temporal information diffusion process behind observed events of an information cascade, such as the retweets of a tweet. To this end, most existing methods either adopt recurrent networks to capture the temporal dynamics from the first to the last observed event or develop a statistical model based on self-exciting point processes to make predictions. However, information diffusion is intrinsically a complex continuous-time process with irregularly observed discrete events, which is oversimplified using recurrent networks as they fail to capture the irregular time intervals between events, or using self-exciting point processes as they lack flexibility to capture the complex diffusion process. Against this background, we propose ConCat, modeling theContinuous-time dynamics ofCascades for information popularity prediction. On the one hand, it leverages neural Ordinary Differential Equations (ODEs) to model irregular events of a cascade in continuous time based on the cascade graph and sequential event information. On the other hand, it considers cascade events as neural temporal point processes (TPPs) parameterized by a conditional intensity function which can also benefit the popularity prediction task. We conduct extensive experiments to evaluate ConCat on three real-world datasets. Results show that ConCat achieves superior performance compared to state-of-the-art baselines, yielding 2.3%-33.2% improvement over the best-performing baselines across the three datasets.

Monitoring urban flow timely and accurately is crucial for many industrial applications – from urban planning to traffic control in the smart cities. This work introduces a new method for inferring fine-grained urban flow with the internet of mobile things such as taxis and bikes. We tackle the problem from a new perspective and present a novel deep learning method UrbanODE (Urban flow inference with Neural Ordinary Differential Equations). Furthermore, UrbanODE provides a flexible balance between flow inference accuracy and computational efficiency, which is important in computation restricted scenarios such as pervasive edge computing. Extensive evaluations on real-world traffic flow data demonstrate the superiority of the proposed method.

Modeling the information diffusion process is an essential step towards understanding the mechanisms driving the success of information. Existing methods either exploit various features associated with cascades to study the underlying factors governing information propagation, or leverage graph representation techniques to model the diffusion process in an end-to-end manner. Current solutions are only valid for a static and fixed observation scenario and fail to handle increasing observations due to the challenge of catastrophic forgetting problems inherent in the machine learning approaches used for modeling and predicting cascades. To remedy this issue, we propose a novel dynamic information diffusion model CICP (Continual Information Cascades Prediction). CICP employs graph neural networks for modeling information diffusion and continually adapts to increasing observations. It is capable of capturing the correlations between successive observations while preserving the important parameters regarding cascade evolution and transition. Experiments conducted on real-world cascade datasets demonstrate that our method not only improves the prediction performance with accumulated data but also prevents the model from forgetting previously trained tasks.