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DeepCas: an End-to-end Predictor of Information Cascades [article]

Cheng Li, Jiaqi Ma, Xiaoxiao Guo, Qiaozhu Mei
2016 arXiv   pre-print
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

Yingdan Shang, Bin Zhou, Ye Wang, Aiping Li, Kai Chen, Yichen Song, Changjian Lin
2021 Axioms  
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]

Yansong Wang, Xiaomeng Wang, Radosław Michalski, Yijun Ran, Tao Jia
2022 arXiv   pre-print
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

Yun Liu, Zemin Bao, Zhenjiang Zhang, Di Tang, Fei Xiong
2020 Human-Centric Computing and Information Sciences  
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]

Qihang Zhao
2020 arXiv   pre-print
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]

Soheila Molaei, Hadi Zare, Hadi Veisi
2019 arXiv   pre-print
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]

Fan Zhou, Xovee Xu, Goce Trajcevski, Kunpeng Zhang
2020 arXiv   pre-print
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]

Subhabrata Dutta, Sarah Masud, Soumen Chakrabarti, Tanmoy Chakraborty
2020 arXiv   pre-print
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]

Xovee Xu, Fan Zhou, Kunpeng Zhang, Siyuan Liu
2021 arXiv   pre-print
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

Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang
2018 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18  
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

Cheng-Lin Wu, Hsun-Ping Hsieh, Jiawei Jiang, Yi-Chieh Yang, Chris Shei, Yu-Wen Chen
2022 Applied Sciences  
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]

Ramya Akula, Niloofar Yousefi, Ivan Garibay
2019 arXiv   pre-print
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

John Bollenbacher, Diogo Pacheco, Pik-Mai Hui, Yong-Yeol Ahn, Alessandro Flammini, Filippo Menczer
2021 Applied Network Science  
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]

Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang
2018 arXiv   pre-print
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+

Zhiyue Yan, Wenming Cao, Jianhua Ji
2021 Discover Internet of Things  
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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