Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction

Tang, Xiangyun; Liao, Dongliang; Huang, Weijie; Xu, Jin; Zhu, Liehuang; Shen, Meng

doi:10.1609/aaai.v35i1.16137

Cited by 25 publications

(5 citation statements)

References 24 publications

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“…Data-driven Feature learning Captured the representative features to reveal important factors influencing information diffusion [33][34][35] Deep learning Improved prediction accuracy with neural networks [36][37][38][39][40][41] (1) Time-series methods attempt to summarize the pattern of the data and use mathematical expressions to portray the diffusion process of the propagation model over time. Recently, more and more models have been proposed to adapt to the differences between social network and traditional contagion models.…”

Section: Time-series/data-driven Methods Contributions Referencementioning

confidence: 99%

“…Cao et al [37] proposed the DeepHawkes model, which simulated the interpretable factors of the Hawkes process and utilized GRU to model the cascade information. Then, numerous new deep learning models were proposed to extract cascade, user, and some other features, such as HiDAN [38], DyHGCN [39], and TempCas [40]. Further, Liu et al [45] were inspired by the field dynamics theory in psychology and proposed an end-to-end model that includes the fields of extrinsic environment and intrinsic cognition.…”

Section: Time-series/data-driven Methods Contributions Referencementioning

confidence: 99%

See 1 more Smart Citation

A Dynamic Emotional Propagation Model over Time for Competitive Environments

Chen,

Xu,

Cai

et al. 2023

Electronics

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Emotional propagation research aims to discover and show the laws of opinion evolution in social networks. The short-term observation of the emotional propagation process for a predetermined time window ignores situations in which users with different emotions compete over a long diffusion time. To that end, we propose a dynamic emotional propagation model based on an independent cascade. The proposed model is inspired by the interpretable factors of the reinforced Poisson process, portraying the “rich-get-richer” phenomenon within a social network. Specifically, we introduce a time-decay mechanism to illustrate the change in influence over time. Meanwhile, we propose an emotion-exciting mechanism allowing prior users to affect the emotions of subsequent users. Finally, we conduct experiments on an artificial network and two real-world datasets—Wiki, with 7194 nodes, and Bitcoin-OTC, with 5881 nodes—to verify the effectiveness of our proposed model. The proposed method improved the F1-score by 3.5% and decreased the MAPE by 0.059 on the Wiki dataset. And the F1-score improved by 0.4% and the MAPE decreased by 0.013 on the Bitcoin-OTC dataset. In addition, the experimental results indicate a phenomenon of emotions in social networks tending to converge under the influence of opinion leaders after a long enough time.

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Section: Time-series/data-driven Methods Contributions Referencementioning

confidence: 99%

Section: Time-series/data-driven Methods Contributions Referencementioning

confidence: 99%

A Dynamic Emotional Propagation Model over Time for Competitive Environments

Chen,

Xu,

Cai

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…CoupledGNN [40] captured the network structure by two coupled graph neural networks to predict the cascade size. TempCas [41] applies BiGRU and CNN to combine cascade information and temporal information for forwarding prediction. CasFlow [42] uses a hierarchical variational information diffusion model to capture node-level and cascade-level uncertainty and to learn cascade distributions through variational inference and normalized flow.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Dynamic End-to-End Information Cascade Prediction Based on Neural Networks and Snapshot Capture

Han

Meng

2023

Electronics

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Knowing how to effectively predict the scale of future information cascades based on the historical trajectory of information dissemination has become an important topic. It is significant for public opinion guidance; advertising; and hotspot recommendation. Deep learning technology has become a research hotspot in popularity prediction, but for complex social platform data, existing methods are challenging to utilize cascade information effectively. This paper proposes a novel end-to-end deep learning network CAC-G with cascade attention convolution (CAC). This model can stress the global information when learning node information and reducing errors caused by information loss. Moreover, a novel Dynamic routing-AT aggregation method is investigated and applied to aggregate node information to generate a representation of cascade snapshots. Then, the gated recurrent unit (GRU) is employed to learn temporal information. This study’s validity and generalization ability are verified in the experiments by applying CAC-G on two public datasets where CAC-G is better than the existing baseline methods.

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“…In the field of intelligent treatment, patient sequential behaviors were analyzed by supervised reinforcement learning [21] and heterogeneous long short-term memory networks [22]. In the field of the social network, understanding and analyzing the future activities of users is beneficial for many applications such as advertising systems [23], cascade prediction [24]. For example, [25] analyzed the correlations about online activities with Hawkes Process and [26] combined heterogeneous graph neural network to predict the real-time customer response.…”

Section: Related Workmentioning

confidence: 99%

Event-Based Dynamic Graph Representation Learning for Patent Application Trend Prediction

Zou,

Yu,

Sun

et al. 2024

IEEE Trans. Knowl. Data Eng.

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Accurate prediction of what types of patents that companies will apply for in the next period of time can figure out their development strategies and help them discover potential partners or competitors in advance. Although important, this problem has been rarely studied in previous research due to the challenges in modeling companies' continuously evolving preferences and capturing the semantic correlations of classification codes. To fill this gap, we propose an event-based dynamic graph learning framework for patent application trend prediction. In particular, our method is founded on the memorable representations of both companies and patent classification codes. When a new patent is observed, the representations of the related companies and classification codes are updated according to the historical memories and the currently encoded messages. Moreover, a hierarchical message passing mechanism is provided to capture the semantic proximities of patent classification codes by updating their representations along the hierarchical taxonomy. Finally, the patent application trend is predicted by aggregating the representations of the target company and classification codes from static, dynamic and hierarchical perspectives. Experiments on real-world data demonstrate the effectiveness of our approach under various experimental conditions, and also reveal the abilities of our method in learning semantics of classification codes and tracking technology developing trajectories of companies.

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Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction

Cited by 25 publications

References 24 publications

A Dynamic Emotional Propagation Model over Time for Competitive Environments

A Dynamic Emotional Propagation Model over Time for Competitive Environments

Dynamic End-to-End Information Cascade Prediction Based on Neural Networks and Snapshot Capture

Event-Based Dynamic Graph Representation Learning for Patent Application Trend Prediction

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