Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals

• URL: http://arxiv.org/abs/2106.07012v1
• Date: Sun, 13 Jun 2021 14:44:36 GMT
• Title: Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals
• Authors: Subhabrata Dutta, Shravika Mittal, Dipankar Das, Soumen Chakrabarti, Tanmoy Chakraborty
• Abstract summary: We observe two significant temporal signals in cascade data that have not been emphasized or reported to our knowledge. We propose GammaCas, a new cascade growth model as a parametric function of time. Specifically, our model processes these signals through a customized recurrent network, whose states then provide the parameters of the cascade rate function.
• Score: 30.959353518914366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The behaviour of information cascades (such as retweets) has been modelled extensively. While point process-based generative models have long been in use for estimating cascade growths, deep learning has greatly enhanced diverse feature integration. We observe two significant temporal signals in cascade data that have not been emphasized or reported to our knowledge. First, the popularity of the cascade root is known to influence cascade size strongly; but the effect falls off rapidly with time. Second, there is a measurable positive correlation between the novelty of the root content (with respect to a streaming external corpus) and the relative size of the resulting cascade. Responding to these observations, we propose GammaCas, a new cascade growth model as a parametric function of time, which combines deep influence signals from content (e.g., tweet text), network features (e.g., followers of the root user), and exogenous event sources (e.g., online news). Specifically, our model processes these signals through a customized recurrent network, whose states then provide the parameters of the cascade rate function, which is integrated over time to predict the cascade size. The network parameters are trained end-to-end using observed cascades. GammaCas outperforms seven recent and diverse baselines significantly on a large-scale dataset of retweet cascades coupled with time-aligned online news -- it beats the best baseline with an 18.98% increase in terms of Kendall's $\tau$ correlation and $35.63$ reduction in Mean Absolute Percentage Error. Extensive ablation and case studies unearth interesting insights regarding retweet cascade dynamics.

Related papers

• On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction [5.464598715181046]
  Key to accurately predicting information popularity lies in subtly modeling the underlying temporal information diffusion process. We propose ConCat, modeling the Continuous-time dynamics of Cascades for information popularity prediction. We conduct extensive experiments to evaluate ConCat on three real-world datasets.
  arXiv  Detail & Related papers  (2024-09-25T05:08:44Z)
• Hierarchical Information Enhancement Network for Cascade Prediction in Social Networks [51.54002032659713]
  We propose a novel Hierarchical Information Enhancement Network (HIENet) for cascade prediction. Our approach integrates fundamental cascade sequence, user social graphs, and sub-cascade graph into a unified framework.
  arXiv  Detail & Related papers  (2024-03-22T14:57:27Z)
• Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation [33.887641183000255]
  Imputing missing values is crucial for analyzing time series. New Causality-Aware Spatiotemporal Graph Neural Network (Casper) contains a novel Prompt Decoder (PBD) and a Stemporal Causal Attention (SCA)
  arXiv  Detail & Related papers  (2024-03-18T16:57:16Z)
• A Dynamical Model of Neural Scaling Laws [79.59705237659547]
  We analyze a random feature model trained with gradient descent as a solvable model of network training and generalization. Our theory shows how the gap between training and test loss can gradually build up over time due to repeated reuse of data.
  arXiv  Detail & Related papers  (2024-02-02T01:41:38Z)
• Explicit Time Embedding Based Cascade Attention Network for Information Popularity Prediction [12.645792211510276]
  We propose an explicit Time embedding based Cascade Attention Network (TCAN) as a novel popularity prediction architecture for large-scale information networks. TCAN integrates temporal attributes into node features via a general time embedding approach (TE), and then employs a cascade graph attention encoder (CGAT) and a cascade sequence attention encoder (CSAT) to fully learn the representation of cascade graphs and cascade sequences.
  arXiv  Detail & Related papers  (2023-08-19T10:43:11Z)
• Semi-Supervised and Long-Tailed Object Detection with CascadeMatch [91.86787064083012]
  We propose a novel pseudo-labeling-based detector called CascadeMatch. Our detector features a cascade network architecture, which has multi-stage detection heads with progressive confidence thresholds. We show that CascadeMatch surpasses existing state-of-the-art semi-supervised approaches in handling long-tailed object detection.
  arXiv  Detail & Related papers  (2023-05-24T07:09:25Z)
• 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)
• Towards an Understanding of Benign Overfitting in Neural Networks [104.2956323934544]
  Modern machine learning models often employ a huge number of parameters and are typically optimized to have zero training loss. We examine how these benign overfitting phenomena occur in a two-layer neural network setting. We show that it is possible for the two-layer ReLU network interpolator to achieve a near minimax-optimal learning rate.
  arXiv  Detail & Related papers  (2021-06-06T19:08:53Z)
• Neural Pruning via Growing Regularization [82.9322109208353]
  We extend regularization to tackle two central problems of pruning: pruning schedule and weight importance scoring. Specifically, we propose an L2 regularization variant with rising penalty factors and show it can bring significant accuracy gains. The proposed algorithms are easy to implement and scalable to large datasets and networks in both structured and unstructured pruning.
  arXiv  Detail & Related papers  (2020-12-16T20:16:28Z)
• CasGCN: Predicting future cascade growth based on information diffusion graph [5.925905552955426]
  We propose a novel deep learning architecture for cascade growth prediction, called CasGCN. It employs the graph convolutional network to extract structural features from a graphical input, followed by the application of the attention mechanism. We conduct experiments on two real-world cascade growth prediction scenarios.
  arXiv  Detail & Related papers  (2020-09-10T21:20:09Z)
• Cascade-LSTM: Predicting Information Cascades using Deep Neural Networks [0.0]
  We use Long-Short Term Memory (LSTM) neural network techniques to predict two-temporal properties of information cascades. Our approach leads to a classification accuracy of over 73% for information transmitters and 83% for early transmitters in a variety of social platforms.
  arXiv  Detail & Related papers  (2020-04-26T13:17:24Z)

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.