DEIM Forum 2019 P1-142

Towards Future Event Prediction using Graph-LSTM
Peng CHEN† , Adam JATOWT† , and Masatoshi YOSHIKAWA†† Department of Social Informatics, Kyoto University
Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
E-mail: chenpeng.acmer@yahoo.com, adam@dl.kuis.kyoto-u.ac.jp, yoshikawa@i.kyoto-u.ac.jp
Abstract Predicting what happens next between countries of interest is meaningful for social scientists and policy makers. By using auto-coded events database GDELT (Global Data on Events, Location, and Tone), which
records what happened between all the countries in the past few decades, we can predict events in a future time
period. However, in the predicting process, we should not ignore the interaction of events between other countries.
Therefore, we first build a graph composed of several closely related countries, and use a Graph-LSTM to predict
future events between a specific pair of countries on a graph composed of several countries, also taking into account
events happened between them and their neighbors.
Key words event prediction, GDELT, graph structure, LSTM
compared to vanilla RNN/LSTM that only learns knowledge

1 Introduction

from event records between a specific pair of countries.

Predicting what happens next between two countries of interest is meaningful for social scientists, journalists and policy makers, which helps us understand trends in international

2 Related WorkA number of existing works have researched how to makerelations. In recent years, many attempts have been madeuse of the GDELT dataset.

to predict future events [1,2,3]. In the prediction tasks, one

learning methods have been used. For example, Phua et al.

of the most challenging steps is to collect data and build andeveloped decision trees to predict the Singapore stock mar-applicable dataset. Fortunately, with the emergence of more

ket’s Straits Time Index using the GDELT dataset [6], Qiao

and more open-source global event databases, we are able toet al. used the GDELT for predicting social unrest events

access records of events happening around the world. It isacross five major nations in Southeast Asia with Hidden

worth mentioning that the GDELT (Global Data on Events,Markov Model (HMM) [2], and Yonamine utilized a statis-Location, and Tone) dataset, one of the largest global event

tical method, named Autoregressive Fractionally Integrated

dataset, is freely available and records a tremendous amountMoving Average (ARFIMA) model, to predict violence lev-of events from a variety of international news sources withels in Afghanistan. More recently, Smith et al. explored thedaily updates

use of Neural Networks (RNNs and LSTMs) for predicting

Some traditional machine-

In the past few years, Recurrent Neural Networks (RNNs)

the number of conflict events in Afghanistan, compared withhave proved their superior ability to perform predictions with

ARFIMA model[3]. On the other hand, many previous works

sequential data on a number of sequence-based learning prob-

have used other data sets for event prediction [6,7,8].

lems[4]. Besides, Long Short-Term Memory (LSTMs), as a
special kind of RNN, are capable of learning long-term dependencies and are now widely used because of their outstanding performance on a large variety of problems[.
In this paper, for data, we exploit the GDELT dataset to3 Long Short-Term Memory Networks
Recurrent Neural Networks (RNNs) are capable of processing sequential data with variable length by applying a single
transition function on hidden states recursively.

build our data collection that is a subset of the GDELT, fo-

LSTMs are explicitly designed to avoid the long-term de-cusing on events happened between several closely related

pendency problem that exists in standard RNNs [5]. For thecountries. For prediction, we develop a Graph-LSTM based

structure of LSTMs, there are five components in a LSTM

predictive framework to predict future events between a pair

unit: an input gate it , a forget gate ft , an output gate ot , aof countries based on events which happened, and also lever-memory cell ct , and a hidden state ht . They are all vectos

aging history event records of several countries that are

in Rk , where k is the dimension of hidden state.

closely related to them.

Our proposal is more advanced

Each step in LSTM’s recursive process can be defined as

a collection of transition functions as follows:

Xnum(pret )it = σ(W t xt +(Uki hk,t−1 + bik ))

(7)

k=1

it = σ(W t xt + U i ht−1 + bi )
ff(1)

f

ft = σ(W xt + U ht−1 + b )(2)

ot = σ(W o xt + U o ht−1 + bo )

(3)

uuu

ut = tanh(W xt + U ht−1 + b )(4)

ct = it ⊙ ut + ft ⊙ ct−1

(5)

ht = ot ⊙ tanh(ct )Xnum(pret )ft = σ(W f xt +(Ukf hk,t−1 + bfl ))(8)(Uko hk,t−1 + bok ))

(9)

k=1

Xnum(pret )ot = σ(W o xt +k=1Xnum(pret )ut = tanh(W u xt +(Uku hk,t−1 + buk ))

(10)

k=1

(6)
Xnum(pret )where xt is the current input at time step t, σ refers to thec t = i t ⊙ ut +sigmoid function, ⊙ denotes elementwise multiplicationm,
and tanh represents hyperbolic tangent. As can be seen,ft ⊙ ck,t−1(11)

k=1ht = ot ⊙ tanh(ct )(12)when a input vector xt comes in, the input gate decides which

where a central LSTM unit takes current input xt andvalues in the input will be updated, then the forget gate con-multiple hidden states from predecessors as input. This kind

trols how much previous information from ct−1 is forgotten,of unit is capable of learning knowledge from the hidden

and the output gate filters the memory state ct , creating thestates from traditional LSTM units, that is to say, the cen-next hidden state ht .

tral LSTM can not only learn from its own sequential in-

4 Graph-LSTM

put but also several other sequential inputs. Besides, plentyof parameters inside the central LSTM allow the networksIntuitively, one of the limitations of the conventional

learning how to make use of multiple sequential data.

LSTM architectures is they can only handle single sequential
input. Recently, several structural variants of LSTMs have

5 Data Representation

been proposed. Tai et al. introduced the tree-structured

GDELT provides auto-coded event records all over theLSTM, in which a LSTM unit may have several precedents

world, which contain various valuable details such as event[10]. Liang et al. first proposed graph LSTM for semanticcode, actors involved, country code and so on. By using

object parsing in the field of image processing, which ex-

GDELT, we can collect event records related to some enti-tends the traditional LSTMs from sequential data to generalties we are interested in. In this paper, we focus on fourgraph-structured data [12]. Moreover, Peng et al. extended

major countries: Japan, India, China and USA, and try to

the use of graph LSTM to cross-sentence relation extraction

predict events that will happened between two countries of[11].

them.

In this paper, we use a designed graph LSTM for future

Each event record has an attribute called goldstein scale

event prediction using multiple sequence data. In the graphthat is a numeric score from -10 to +10 (negative for con-

LSTM we designed, there are two kinds of units, one is tra-flicts and positive for cooperation), capturing the theoretical

ditional LSTM unit, the other is called central LSTM unit.

potential impact that type of event will have on the stabil-

A central LSTM unit has multiple predecessors pret , which

ity of a country. Another attribute, quad class, aggregatesmeans each central unit has multiple inputs from the previ-goldstein scale into 4 classes: 1 for verbal cooperation, 2 forous time step. There are several traditional LSTM units andmaterial cooperation, 3 for verbal conflict, and 4 for materiala central LSTM unit in one layer of the graph LSTM.

conflict.

The structure of the graph LSTM is shown below:

In the experiment, the numbers of occurrences of thesefour kinds of events per week are counted. Having a sequence of event counts, the model predicts the number ofmaterial conflict events (quad class 4) in the next week. Obviously, this is a coarse-grained prediction, which provides
future trends in relationship between two countries. Due toThe following equations describe the transition process ina central unit:

time constraints, we leave more suitable data representations
and more fine-grained predictions for future work.

6 Experiments
Define all events into 4 classes: 1=Verbal Cooperation,
2=Material Cooperation, 3=Verbal Conflict, 4=MaterialConflict. In the preliminary experiment, we focus on thecountry pair: (USA, Japan), and we count the number offour kinds of events that happened between USA and Japanin each week. For prediction, the number of material conflict
events in the 16th week is predicted by the model based on
the previous 15 weeks’ data.
Two kinds of models are evaluated, vanilla LSTM and thegraph LSTM. For vanilla LSTM, we simply use the data ofAs is shown in the figure, the performance of graph LSTM

the country pair (USA, Japan) as input that includes events

is no better than traditional LSTM, and graph LSTM took

happened between these two countries. For graph LSTM, themore time to converge. However, trying to consider the inter-data of (USA, Japan) is fed into the central LSTM while theaction of events between related countries can be reasonable

data of other closely related country pairs, (Japan, China),and helpful. Because in most major events such as mate-(Japan, India), (USA, China) and (USA, India) are taken as

rial conflict events, there are multiple countries involved, notinputs to other parallel LSTMs in the graph LSTM model.

only two countries. Even some particularly significant events

The following figure shows a test result from LSTM:

occur in a global context.

7 Future Work
There are still many shortcomings in our model.

Atpresent, we manually select closely related countries which
are sometimes inappropriate. Ideally, we are supposed toconsider as many counties as possible. On the other hand, weneed to be more careful when combining hidden states fromseveral LSTMs. Currently, we simply add them together
which ignore the weight of each state. Obviously, a more
beautiful and reasonable combination need to be designed.
For future work, we will build a relational graph of countries,
where the dotted blue line shows the predicted number ofmaterial conflicts, and the orange line represents the groundtruth.
Training data consists of events that happened from2005/01/01 to 2016/06/01 (595 weeks), and test set includes
data from 2016/06/01 to 2018/12/01 (130 weeks). The size
ratio of training set and test set is about 8:2. In evaluation,

trying to select more suitable neighbors of two countries. For
hidden state combination, the attention mechanism can be apotential solution, which transforms the simple summation
into an adaptive weighted summation.

8 Acknowledgments
This research has been supported by MIC SCOPE(#171507010).

we use average Mean Squared Error (MSE) to evaluate theloss between prediction results and the ground truth. Traditional LSTM and Graph-LSTM are both used to performprediction for comparison.
The losses after convergence become 1684.9 for LSTM, and1718.51 for graph LSTM. The convergence process comparison is shown in the following figure:

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