Neural Temporal Point Processes for Forecasting Directional Relations in Evolving Hypergraphs

• URL: http://arxiv.org/abs/2301.12210v3
• Date: Wed, 18 Dec 2024 14:36:47 GMT
• Title: Neural Temporal Point Processes for Forecasting Directional Relations in Evolving Hypergraphs
• Authors: Tony Gracious, Arman Gupta, Ambedkar Dukkipati,
• Abstract summary: We provide a comprehensive solution to the problem of forecasting directional relations in a general setting.<n>The number of possible hyperedges is exponential in the number of nodes at each event time.<n>We propose a sequential generative approach that segments the forecasting process into multiple stages.
• Score: 10.803714426078642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forecasting relations between entities is paramount in the current era of data and AI. However, it is often overlooked that real-world relationships are inherently directional, involve more than two entities, and can change with time. In this paper, we provide a comprehensive solution to the problem of forecasting directional relations in a general setting, where relations are higher-order, i.e., directed hyperedges in a hypergraph. This problem has not been previously explored in the existing literature. The primary challenge in solving this problem is that the number of possible hyperedges is exponential in the number of nodes at each event time. To overcome this, we propose a sequential generative approach that segments the forecasting process into multiple stages, each contingent upon the preceding stages, thereby reducing the search space involved in predictions of hyperedges. The first stage involves a temporal point process-based node event forecasting module that identifies the subset of nodes involved in an event. The second stage is a candidate generation module that predicts hyperedge sizes and adjacency vectors for nodes observing events. The final stage is a directed hyperedge predictor that identifies the truth by searching over the set of candidate hyperedges. To validate the effectiveness of our model, we compiled five datasets and conducted an extensive empirical study to assess each downstream task. Our proposed method achieves a performance gain of 32\% and 41\% compared to the state-of-the-art pairwise and hyperedge event forecasting models, respectively, for the event type prediction.

Related papers

• SPHINX: Structural Prediction using Hypergraph Inference Network [19.853413818941608]
  We introduce Structural Prediction using Hypergraph Inference Network (SPHINX), a model that learns to infer a latent hypergraph structure in an unsupervised way. We show that the recent advancement in k-subset sampling represents a suitable tool for producing discrete hypergraph structures. The resulting model can generate the higher-order structure necessary for any modern hypergraph neural network.
  arXiv  Detail & Related papers  (2024-10-04T07:49:57Z)
• DeTra: A Unified Model for Object Detection and Trajectory Forecasting [68.85128937305697]
  Our approach formulates the union of the two tasks as a trajectory refinement problem. To tackle this unified task, we design a refinement transformer that infers the presence, pose, and multi-modal future behaviors of objects. In our experiments, we observe that ourmodel outperforms the state-of-the-art on Argoverse 2 Sensor and Open dataset.
  arXiv  Detail & Related papers  (2024-06-06T18:12:04Z)
• Repeat-Aware Neighbor Sampling for Dynamic Graph Learning [8.975322389416734]
  Existing works obtain the evolving patterns mainly depending on the most recent neighbor sequences. We argue that whether two nodes will have interaction with each other in the future is highly correlated with the same interaction that happened in the past. This paper presents RepeatMixer, which considers evolving patterns of first and high-order repeat behavior in the neighbor sampling strategy and temporal information learning.
  arXiv  Detail & Related papers  (2024-05-24T03:24:29Z)
• Interaction Event Forecasting in Multi-Relational Recursive HyperGraphs: A Temporal Point Process Approach [12.142292322071299]
  This work addresses the problem of forecasting higher-order interaction events in multi-relational recursive hypergraphs. The proposed model, textitRelational Recursive Hyperedge Temporal Point Process (RRHyperTPP), uses an encoder that learns a dynamic node representation based on the historical interaction patterns. We have experimentally shown that our models perform better than previous state-of-the-art methods for interaction forecasting.
  arXiv  Detail & Related papers  (2024-04-27T15:46:54Z)
• Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning [57.35554450622037]
  We propose a novel hyperedge prediction framework (CASH) CASH employs context-aware node aggregation to capture complex relations among nodes in each hyperedge for (C1) and (2) self-supervised contrastive learning in the context of hyperedge prediction to enhance hypergraph representations for (C2) Experiments on six real-world hypergraphs reveal that CASH consistently outperforms all competing methods in terms of the accuracy in hyperedge prediction.
  arXiv  Detail & Related papers  (2023-09-11T20:06:00Z)
• Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection [54.041049052843604]
  We present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection. First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network. Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
  arXiv  Detail & Related papers  (2023-07-01T13:53:14Z)
• DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction [53.76601630407521]
  We propose a hypergraph network model called DisenHCN to bridge the gaps in existing solutions. In particular, we first unify fine-grained user similarity and the complex matching between user preferences andtemporal activity into a heterogeneous hypergraph. We then disentangle the user representations into different aspects (location-aware, time-aware, and activity-aware) and aggregate corresponding aspect's features on the constructed hypergraph.
  arXiv  Detail & Related papers  (2022-08-14T06:51:54Z)
• Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction [52.0977259978343]
  This paper proposes a Continuous-time and Multi-level dynamic graph representation learning method for Origin-Destination demand prediction (CMOD) The state vectors keep historical transaction information and are continuously updated according to the most recently happened transactions. Experiments are conducted on two real-world datasets from Beijing Subway and New York Taxi, and the results demonstrate the superiority of our model against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-06-30T03:37:50Z)
• Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction [12.72319550363076]
  This paper provides a dynamic graph representation learning framework for OD demands prediction. In particular, a hierarchical memory updater is first proposed to maintain a time-aware representation for each node. Atemporal propagation mechanism is provided to aggregate representations of neighbor nodes along a randomtemporal route. An objective function is designed to derive the future OD demands according to the most recent node.
  arXiv  Detail & Related papers  (2022-05-29T07:52:35Z)
• CEP3: Community Event Prediction with Neural Point Process on Graph [59.434777403325604]
  We propose a novel model combining Graph Neural Networks and Marked Temporal Point Process (MTPP) Our experiments demonstrate the superior performance of our model in terms of both model accuracy and training efficiency.
  arXiv  Detail & Related papers  (2022-05-21T15:30:25Z)
• GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning [37.64048110462638]
  GroupNet is a multiscale hypergraph neural network that captures both pair-wise and group-wise interactions. We apply GroupNet into both CVAE-based prediction system and previous state-of-the-art prediction systems. We show that with GroupNet, the CVAE-based prediction system outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-19T09:36:20Z)
• Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecasting [8.680676599607123]
  This paper proposes a temporal point process model for hyperedge prediction to address these problems. As far as our knowledge, this is the first work that uses the temporal point process to forecast hyperedges in dynamic networks.
  arXiv  Detail & Related papers  (2021-12-19T14:24:37Z)
• Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report [70.7321040534471]
  Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. There is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting.
  arXiv  Detail & Related papers  (2021-09-01T09:52:31Z)
• Predicting Influential Higher-Order Patterns in Temporal Network Data [2.5782420501870287]
  We propose eight centrality measures based on MOGen, a multi-order generative model that accounts for all paths up to a maximum distance but disregards paths at higher distances. We show that MOGen consistently outperforms both the network model and path-based prediction.
  arXiv  Detail & Related papers  (2021-07-26T10:44:46Z)
• Predicting Temporal Sets with Deep Neural Networks [50.53727580527024]
  We propose an integrated solution based on the deep neural networks for temporal sets prediction. A unique perspective is to learn element relationship by constructing set-level co-occurrence graph. We design an attention-based module to adaptively learn the temporal dependency of elements and sets.
  arXiv  Detail & Related papers  (2020-06-20T03:29:02Z)
• Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction [69.1473775184952]
  We introduce a realistic problem of few-shot out-of-graph link prediction. We tackle this problem with a novel transductive meta-learning framework. We validate our model on multiple benchmark datasets for knowledge graph completion and drug-drug interaction prediction.
  arXiv  Detail & Related papers  (2020-06-11T17:42:46Z)
• A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction [74.00750936752418]
  We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC) First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
  arXiv  Detail & Related papers  (2020-03-13T04:35:50Z)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.