Dyadic event attribution in social networks with mixtures of hawkes processes

Li, Liangda; Zha, Hongyuan

doi:10.1145/2505515.2505609

Cited by 30 publications

(22 citation statements)

References 14 publications

(15 reference statements)

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“…In social media such approaches have been used extensively [18,30,31], mainly due to their capability to model phenomena like user interactions and diffusion susceptibility. Hidden network properties, such as connexions between individuals [23,24] or the evolution of the diffusion network topology [13], have been uncovered using generative methods, such as selfexciting point processes. The main difficulty with such approaches is scalability, since each social phenomenon must be accounted manually in the model.…”

Section: Related Workmentioning

confidence: 99%

Feature Driven and Point Process Approaches for Popularity Prediction

Mishra

Rizoiu

Xie

2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

153

168

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Predicting popularity, or the total volume of information outbreaks, is an important subproblem for understanding collective behavior in networks. Each of the two main types of recent approaches to the problem, feature-driven and generative models, have desired qualities and clear limitations. This paper bridges the gap between these solutions with a new hybrid approach and a new performance benchmark. We model each social cascade with a marked Hawkes self-exciting point process, and estimate the content virality, memory decay, and user influence. We then learn a predictive layer for popularity prediction using a collection of cascade history. To our surprise, Hawkes process with a predictive overlay outperform recent feature-driven and generative approaches on existing tweet data [43] and a new public benchmark on news tweets. We also found that a basic set of user features and event time summary statistics performs competitively in both classification and regression tasks, and that adding point process information to the feature set further improves predictions. From these observations, we argue that future work on popularity prediction should compare across feature-driven and generative modeling approaches in both classification and regression tasks

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Section: Related Workmentioning

confidence: 99%

Feature Driven and Point Process Approaches for Popularity Prediction

Mishra

Rizoiu

Xie

2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

153

168

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“…People find self-exciting point processes naturally suitable to model continuous-time events where the occurrence of one event can affect the likelihood of subsequent events in the future. One important self-exciting process is Hawkes process, which is first used to analyze earthquakes [25,37], and then widely applied to many different areas, such as market modeling [13,3], crime modeling [29], terrorist [26], conflict [35,21], and viral videos on the Web [10]. A novel Hawkes model was also proposed to model both temporal and textual information in viral [34].…”

Section: Related Workmentioning

confidence: 99%

Identifying and labeling search tasks via query-based hawkes processes

Deng

Dong

et al. 2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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We consider a search task as a set of queries that serve the same user information need. Analyzing search tasks from user query streams plays an important role in building a set of modern tools to improve search engine performance. In this paper, we propose a probabilistic method for identifying and labeling search tasks based on the following intuitive observations: queries that are issued temporally close by users in many sequences of queries are likely to belong to the same search task, meanwhile, different users having the same information needs tend to submit topically coherent search queries. To capture the above intuitions, we directly model query temporal patterns using a special class of point processes called Hawkes processes, and combine topic models with Hawkes processes for simultaneously identifying and labeling search tasks. Essentially, Hawkes processes utilize their self-exciting properties to identify search tasks if influence exists among a sequence of queries for individual users, while the topic model exploits query co-occurrence across different users to discover the latent information needed for labeling search tasks. More importantly, there is mutual reinforcement between Hawkes processes and the topic model in the unified model that enhances the performance of both. We evaluate our method based on both synthetic data and real-world query log data. In addition, we also apply our model to query clustering and search task identification. By comparing with state-of-the-art methods, the results demonstrate that the improvement in our proposed approach is consistent and promising.

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“…In order to fully exploit the social ties between users and information in social networks, we base our trend detection algorithm on information diffusion models [5], [6], [7], [8], and more specifically on a Hawkes-based model for information diffusion in social networks [9], [10], [11], [12]. The Hawkesbased model allows: 1) leveraging on the knowledge of the influences between users and contents, 2) to fully explore the real time of broadcasts, 3) leveraging on the knowledge of users intrinsic (or exogenous) rates.…”

Section: Introductionmentioning

confidence: 99%

Trend detection in social networks using Hawkes processes

Pinto

Chahed

Altman

2015

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

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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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Dyadic event attribution in social networks with mixtures of hawkes processes

Cited by 30 publications

References 14 publications

Feature Driven and Point Process Approaches for Popularity Prediction

Feature Driven and Point Process Approaches for Popularity Prediction

Identifying and labeling search tasks via query-based hawkes processes

Trend detection in social networks using Hawkes processes

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