Learning Dynamic Context Graphs for Predicting Social Events

Deng, Songgaojun; Rangwala, Huzefa; Ning, Yue

doi:10.1145/3292500.3330919

Cited by 77 publications

(53 citation statements)

References 18 publications

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“…In Deng et al [2019] the authors developed a dynamic vocabulary graph and modeled a GCN to identify key events and understand their evolution. Their proposal was evaluated within protest events datasets.…”

Section: Related Workmentioning

confidence: 99%

“…Existing methods of network embeddings are offline, which requires repeating the entire process when new nodes are added to the network. Some pipelines may include different techniques for dynamic insertions [Deng et al 2019;, but they must be modeled apart from the network embedding. On the other hand, TPHIN is naturally incremental, as it takes advantage of a pre-trained feature semantic space from neural language models by propagating from its initial embeddings to the entire network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Commodities trend link prediction on heterogeneous information networks

Carmo

Filho

Marcacini

2021

Anais Do IX Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2021)

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Events can be defined as an action or a series of actions that have a determined theme, time, and place. Event analysis tasks for knowledge extraction from news and social media have been explored in recent years. However, there are still few studies that aim to enrich predictive models using event data. In particular, agribusiness events have multiple components to be considered for a successful prediction model. For example, price trend predictions for commodities can be performed through time series analysis of prices, but we can also consider events that represent knowledge about external factors during the training step of predictive models. In this paper, we present a method for integrating events into trend prediction tasks. First, we propose to model events and time-series information through heterogeneous information networks (HIN) that allow multiple components to be directly modeled through multi-type nodes and edges. Second, we learn features from HIN through network embedding methods, i.e., network nodes are mapped to a dense vector of features. In particular, we propose a network embedding method that propagates the semantic of the pre-trained neural language models to a heterogeneous information network and evaluates its performance in a trend link prediction. We show that the use of our proposed model language-based embedding propagation is competitive with state-of-art network embeddings algorithms. Moreover, our proposal performs network embedding incrementally, thereby allowing new events to be inserted in the same semantic space without rebuilding the entire network embedding.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Commodities trend link prediction on heterogeneous information networks

Carmo

Filho

Marcacini

2021

Anais Do IX Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2021)

View full text Add to dashboard Cite

show abstract

“…Especially since 2013, with the popularization of big data technology, big data-driven social unrest event prediction research has ushered in a period of vigorous development. In the conferences such as SIGKDD [11,12], WWW [13,14], SDM [15], AAAI [1,16], and journals such as IEEE Trans. [17,18], more than 30 related works have been published in succession, and the degree of attention is evident.…”

Section: Related Workmentioning

confidence: 99%

“…Wu et al [41] used the "Protest Participation eory" proposed in the field of political science, combined with the SVM support vector machine model, to conduct early warning research on social unrest events. Deng et al [12] extracted and learned graph representations from historical event documents. By employing the hidden word graph features, the model predicts the occurrence of future events and identifies sequences of dynamic graphs as event context.…”

Section: Related Workmentioning

confidence: 99%

Learning Evolutionary Stages with Hidden Semi-Markov Model for Predicting Social Unrest Events

Qiao

Zhang

Deng

2020

Discrete Dynamics in Nature and Society

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Social unrest events are common happenings in modern society which need to be proactively handled. An effective method is to continuously assess the risk of upcoming social unrest events and predict the likelihood of these events. Our previous work built a hidden Markov model- (HMM-) based framework to predict indicators associated with country instability, leaving two shortcomings which can be optimized: omitting event participants’ interaction and implicitly learning the state residence time. Inspired by this, we propose a new prediction framework in this paper, using frequent subgraph patterns and hidden semi-Markov models (HSMMs). The feature called BoEAG (Bag-of-Event-Association-subGraph) is constructed based on frequent subgraph mining and the bag of word model. The new framework leverages the large-scale digital history events captured from GDELT (Global Data on Events, Location, and Tone) to characterize the transitional process of the social unrest events’ evolutionary stages, uncovering the underlying event development mechanics and formulating the social unrest event prediction as a sequence classification problem based on Bayes decision. Experimental results with data from five main countries in Southeast Asia demonstrate the effectiveness of the new method, which outperforms the traditional HMM by 5.3% to 16.8% and the logistic regression by 11.2% to 43.6%.

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“…As a special knowledge graph, Event Graph (EG) integrates event and conceptual knowledge to address the absoluteness of static knowledge in conceptual knowledge, building relationships between event entities, conceptual entities, and conceptual entities and event entities. Similar to human thinking, EG can express more complex semantic information (Deng et al, 2019). The information organization form of EG makes it suitable for storage and reuse of complex knowledge represented by the C-RFBS model.…”

Section: Introductionmentioning

confidence: 99%

A C-RFBS model for the efficient construction and reuse of interpretable design knowledge records across knowledge networks

Zhang

Wang

Zhai³

et al. 2021

Systems Science & Control Engineering

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Effective and efficient provision and reuse of knowledge across a knowledge network in the global value chain still faces two challenges, namely the interpretability of model and the efficacy of construction. This research aims to address these challenges by proposing a novel representation model for design knowledge. First, a knowledge representation model based on the integration of the cognitive process theory and the Requirement-Function-Behavior-Structure model (C-RFBS model) is proposed to incorporate key elements from the cognitive process of designers to capture the rationale of deliberation and the context of decision-making, which the knowledge records created become more interpretable. Second, knowledge graph is employed to improve the productivity of knowledge records creation, storage and exploration. On this basis, we describe the creation of knowledge records using the C-RFBS model as well as the computational framework and methods for storing knowledge using knowledge graph. The proposed model and methods are implemented in a knowledge retrieval system on which we have conducted a fork design case study to evaluate and demonstrate the models and methods. As shown in the evaluation, the proposed model can effectively support knowledge elicitation and achieved improved performance in terms of knowledge retrieval through incorporating knowledge graph.

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Learning Dynamic Context Graphs for Predicting Social Events

Cited by 77 publications

References 18 publications

Commodities trend link prediction on heterogeneous information networks

Commodities trend link prediction on heterogeneous information networks

Learning Evolutionary Stages with Hidden Semi-Markov Model for Predicting Social Unrest Events

A C-RFBS model for the efficient construction and reuse of interpretable design knowledge records across knowledge networks

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