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DeepCas: an End-to-end Predictor of Information Cascades
[article]
2016
arXiv
pre-print
Preserved Fulltext
We present algorithms that learn the representation of cascade graphs in an end-to-end manner, which significantly improve the performance of cascade prediction over strong baselines that include feature ...
Inspired by the recent successes of deep learning in multiple data mining tasks, we investigate whether an end-to-end deep learning approach could effectively predict the future size of cascades. ...
DISCUSSION AND CONCLUSION We present the first end-to-end, deep learning based predictor of information cascades. ...
arXiv:1611.05373v1
fatcat:iqz3l7drxnf6hmgfmh7moa3aei
Popularity Prediction of Online Contents via Cascade Graph and Temporal Information
2021
Axioms
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The results show that temporal information rather than cascade graph information is a better predictor for popularity. ...
Predicting the popularity of online content is an important task for content recommendation, social influence prediction and so on. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/axioms10030159
fatcat:lb4sby4s2bcxlifbywiz55saqm
CasSeqGCN: Combining Network Structure and Temporal Sequence to Predict Information Cascades
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2110.06836v1 fatcat:l3gacugjybcktnkxo6irnht6rq
One important task in the study of information cascade is to predict the future recipients of a message given its past spreading trajectory. ...
We explicitly design an experiment to show the quality of the cascade representation learned by our approach is better than other methods. ...
DeepCas [11] is the first end-to-end model that applies deep learning technology to the cascade prediction problem. ...
arXiv:2110.06836v2
fatcat:svtsh5iz3nfydagw5v5eea6moe
Information cascades prediction with attention neural network
2020
Human-Centric Computing and Information Sciences
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This paper presents a novel method for predicting the increment size of the information cascade based on an end-to-end neural network. ...
Learning the representation of a cascade in an end-to-end manner circumvents the difficulties inherent to blue the design of hand-crafted features. ...
DeepCas [9] This is the first end-to-end, deep learning method for information cascades prediction. ...
doi:10.1186/s13673-020-00218-w
fatcat:dzdoxvdyoncjhjyxdrb6fzgaze
Utilizing Citation Network Structure to Predict Citation Counts: A Deep Learning Approach
[article]
2020
arXiv
pre-print
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This paper proposes an end-to-end deep learning network, DeepCCP, which combines the effect of information cascade and looks at the citation counts prediction problem from the perspective of information ...
It is also the evaluation standard for decision-making such as promotion and allocation of funds. Therefore, how to measure the quality of an academic paper is very important. ...
information of the cascade network in an end-to-end manner( [70] , [65] , [71] , [72] , [73] ). ...
arXiv:2009.02647v1
fatcat:urhn5fl3sre5viw47ywe4g5iua
Deep Learning Approach on Information Diffusion in Heterogeneous Networks
[article]
2019
arXiv
pre-print
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The proposed approach enables us to apply it on different information diffusion tasks such as topic diffusion and cascade prediction. ...
At first, the functional heterogeneous structures of the network are learned by a continuous latent representation through traversing meta-paths with the aim of global end-to-end viewpoint. ...
These models learn to predict information cascade in an end-to-end manner. ...
arXiv:1902.08810v1
fatcat:wqpp552pdraffh7pcp3dfzv46a
A Survey of Information Cascade Analysis: Models, Predictions and Recent Advances
[article]
2020
arXiv
pre-print
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opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. ...
Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. ...
ACKNOWLEDGMENTS We thank all doctors, nurses and medical personnel who are on the front line of the war against COVID-19. ...
arXiv:2005.11041v2
fatcat:wxn5azl25vhe7pnulkfpehezey
Deep Exogenous and Endogenous Influence Combination for Social Chatter Intensity Prediction
[article]
2020
arXiv
pre-print
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Given streams of timestamped news articles and discussions, the task is to observe the streams for a short period leading up to a time horizon, then predict chatter: the volume of discussions through a ...
To address the three limitations noted above, we propose a novel framework, ChatterNet, which, to our knowledge, is the first that can model and predict user engagement without considering the underlying ...
Our third external baseline is an adaptation of the Relativistic Gravitational Network [7] , primarily designed to predict user engagement behavior over Reddit.
DeepCas. ...
arXiv:2006.07812v1
fatcat:izr74a2gr5crrf5onjuaw3wowi
CCGL: Contrastive Cascade Graph Learning
[article]
2021
arXiv
pre-print
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In particular, CCGL first designs an effective data augmentation strategy to capture variation and uncertainty. ...
Supervised learning, while prevalent for information cascade modeling, often requires abundant labeled data in training, and the trained model is not easy to generalize across tasks and datasets. ...
The learning ability of end-to-end models is constrained by batch sizes. ...
arXiv:2107.12576v1
fatcat:kb3si37j65gntar64i63aq7hzi
DeepInf
2018
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18
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2018 pre-print arXiv:1807.05560v1 fatcat:nl4vcanunbfddcgrsi7il6qwge
Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting ...
Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly ...
Jiezhong Qiu and Jie Tang are supported by NSFC 61561130160 and National Basic Research Program of China 2015CB358700. ...
doi:10.1145/3219819.3220077
dblp:conf/kdd/QiuTMDW018
fatcat:s67iisu62zeuna3os7545fh7yu
MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter
2022
Applied Sciences
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To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. ...
To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app12010453
fatcat:ryfj6ggqjrejxhzgllxavwatye
DeepFork: Supervised Prediction of Information Diffusion in GitHub
[article]
2019
arXiv
pre-print
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Often it is hard to barricade this diffusion prior to the significant occurrence of chaos, be it a social media or an online coding platform. ...
DeepFork aids in understanding information spread and evolution through a bipartite network of users and repositories i.e., information flow from a user to repository to user. ...
: is another end to end deep learning-based framework that attempted to fill the gap between prediction and understanding of information cascades. ...
arXiv:1910.07999v1
fatcat:uqyk25efyjcc7lyw5dsssbqtru
On the challenges of predicting microscopic dynamics of online conversations
2021
Applied Network Science
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However, predicting the macroscopic structure of conversations does not guarantee an accurate reconstruction of their microscopic evolution. ...
We show that it is able to predict both macroscopic features of the final trees and near-future microscopic events with moderate accuracy. ...
We are also grateful to Emilio Ferrara, Kristina Lerman, Jim Blythe, Goran Muric, and Alexey Tregubov for useful discussions. ...
doi:10.1007/s41109-021-00357-8
fatcat:zv2q5qhv55ehjmb2nz5dwjlzd4
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
[article]
2018
arXiv
pre-print
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Graph embedding is an effective yet efficient way to solve the graph analytics problem. ...
Graph is an important data representation which appears in a wide diversity of real-world scenarios. ...
., a recurrent neural network model similar to LSTM) is used to embed information cascade paths. ...
arXiv:1709.07604v3
fatcat:6w42r4k6rvbodmnuerbqqrxynq
Social behavior prediction with graph U-Net+
2021
Discover Internet of Things
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Existing works use graph representation learning methods to give each node an embedding vector, then update the node representations by designing different information passing and aggregation mechanisms ...
In order to improve the feature extraction capability in convolutional process and eliminate the effect of over-smoothing problem, we introduce the bilinear information aggregator and NodeNorm normalization ...
[16] proposed an end-to-end predictor, DeepCas, which studies the diffusion cascade of social influence by incorporating bidirectional-GRU and Node2Vec to achieve effective prediction of user behavior ...
doi:10.1007/s43926-021-00018-3
fatcat:f7srl4pb2vd43cty32wcydbpzy
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