research-article

Trend detection in social networks using Hawkes processes

Authors:

Julio Cesar Louzada Pinto,

Eitan AltmanAuthors Info & Claims

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Pages 1441 - 1448

https://doi.org/10.1145/2808797.2814178

Published: 25 August 2015 Publication History

Abstract

We develop in this paper a trend detection algorithm, designed to find trendy topics being disseminated in a social network. We assume that the broadcasts of messages in the social network is governed by a self-exciting point process, namely a Hawkes process, which takes into consideration the real broadcasting times of messages and the interaction between users and topics. We formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the time between the detection and the message broadcasts, the distance between the real broadcast intensity and the maximum expected broadcast intensity, and the social network topology. The proposed trend detection algorithm is simple and uses stochastic control techniques in order to calculate the trend indices. It is also fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of data necessary to the detection.

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Cited By

Iamnitchi AHall LHorawalavithana SMubang FNg KSkvoretz J(2023)Modeling information diffusion in social media: data-driven observationsFrontiers in Big Data10.3389/fdata.2023.11351916Online publication date: 17-May-2023
https://doi.org/10.3389/fdata.2023.1135191
Ng KMubang FHall LSkvoretz JIamnitchi A(2023)Experimental evaluation of baselines for forecasting social media timeseriesEPJ Data Science10.1140/epjds/s13688-023-00383-912:1Online publication date: 27-Mar-2023
https://doi.org/10.1140/epjds/s13688-023-00383-9
Ng KHorawalavithana SIamnitchi A(2022)Social media activity forecasting with exogenous and endogenous signalsSocial Network Analysis and Mining10.1007/s13278-022-00927-312:1Online publication date: 8-Aug-2022
https://doi.org/10.1007/s13278-022-00927-3
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Trend detection in social networks using Hawkes processes

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cover image ACM Conferences

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

August 2015

835 pages

ISBN:9781450338547

DOI:10.1145/2808797

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 August 2015

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ASONAM '15: Advances in Social Networks Analysis and Mining 2015

August 25 - 28, 2015

Paris, France

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Iamnitchi AHall LHorawalavithana SMubang FNg KSkvoretz J(2023)Modeling information diffusion in social media: data-driven observationsFrontiers in Big Data10.3389/fdata.2023.11351916Online publication date: 17-May-2023
https://doi.org/10.3389/fdata.2023.1135191
Ng KMubang FHall LSkvoretz JIamnitchi A(2023)Experimental evaluation of baselines for forecasting social media timeseriesEPJ Data Science10.1140/epjds/s13688-023-00383-912:1Online publication date: 27-Mar-2023
https://doi.org/10.1140/epjds/s13688-023-00383-9
Ng KHorawalavithana SIamnitchi A(2022)Social media activity forecasting with exogenous and endogenous signalsSocial Network Analysis and Mining10.1007/s13278-022-00927-312:1Online publication date: 8-Aug-2022
https://doi.org/10.1007/s13278-022-00927-3
Idé TKollias GPhan DAbe NRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Cardinality-regularized hawkes-granger modelProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3540466(2682-2694)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3540466
Alves RLedent AKloft M(2021)Burst-induced Multi-Armed Bandit for Learning RecommendationProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3474250(292-301)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1145/3460231.3474250
Xie LZou SXie YVeeravalli V(2021)Sequential (Quickest) Change Detection: Classical Results and New DirectionsIEEE Journal on Selected Areas in Information Theory10.1109/JSAIT.2021.30729622:2(494-514)Online publication date: Jun-2021
https://doi.org/10.1109/JSAIT.2021.3072962
Jiang YPorter M(2021)Discovering Influence of Yelp Reviews Using Hawkes Point ProcessesIntelligent Systems and Applications10.1007/978-3-030-82199-9_7(84-104)Online publication date: 7-Aug-2021
https://doi.org/10.1007/978-3-030-82199-9_7
Pasha SSolo VGodoy B(2020)An EM Algorithm for the Time-Variant Vector Hawkes Process2020 59th IEEE Conference on Decision and Control (CDC)10.1109/CDC42340.2020.9304188(1726-1731)Online publication date: 14-Dec-2020
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Feng XZhao QMa JJiang G(2020)On modeling and predicting popularity dynamics via integrating generative model and rich featuresKnowledge-Based Systems10.1016/j.knosys.2020.105786(105786)Online publication date: Mar-2020
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De ABhattacharya SGanguly NAndre EKoenig SDastani MSukthankar G(2018)Shaping Opinion Dynamics in Social NetworksProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237899(1336-1344)Online publication date: 9-Jul-2018
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