Modeling Scientific Influence for Research Trending Topic Prediction

Chengyao Chen1∗, Zhitao Wang1∗, Wenjie Li1, Xu Sun2

1Department of Computing, The Hong Kong Polytechnic University, Hong Kong2Department of Computer Science, Peking University, China

{cscchen, csztwang, cswjli}@comp.polyu.edu.hk, xusun@pku.edu.cn

AbstractWith the growing volume of publications in the ComputerScience (CS) discipline, tracking the research evolution andpredicting the future research trending topics are of great im-portance for researchers to keep up with the rapid progressof research. Within a research area, there are many top con-ferences that publish the latest research results. These con-ferences mutually influence each other and jointly promotethe development of the research area. To predict the trend-ing topics of mutually influenced conferences, we proposea correlated neural influence model, which has the ability tocapture the sequential properties of research evolution in eachindividual conference and discover the dependencies amongdifferent conferences simultaneously. The experiments con-ducted on a scientific dataset including conferences in artifi-cial intelligence and data mining show that our model con-sistently outperforms the other state-of-the-art methods. Wealso demonstrate the interpretability and predictability of theproposed model by providing its answers to two questions ofconcern, i.e., what the next rising trending topics are and foreach conference who the most influential peer is.

IntroductionThe research in the Computer Science (CS) discipline hasmade surprising progress over recent years. Accordingly,the number of research papers published on CS venueshas been extremely increasing. Among all the venues, con-ferences are the most representative platforms to displaythe latest research for the fast dissemination (Vrettas andSanderson 2015). The paper collections of conferences oftenhold important clues about the dynamics of research topicsin according research areas. Exploring the research evolu-tion from these conferences and being able to predict fu-ture trending topics are of great significance for a variety ofcommunities. For instance, funding agencies can optimizethe funding allocation according to the promising topics andtechnology companies can adjust the development strategiesin advance.

Considering the fast changing research trends and thegrowing volume of publications, keeping up with the re-search trends is hard even for experts. Previously, promi-nent efforts have been made to detect the existing research

∗Authors contributed equally.Copyright c© 2018, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.

ACL

CVPR

NIPS

𝟐𝟎𝟏𝟒 𝟐𝟎𝟏𝟓

Figure 1: The research evolution of conferences in AI re-search area.

topics through topic modeling (Griffiths and Steyvers 2004;Steyvers et al. 2004) or perform a historical analysis on howresearch topics change in the past (Wang and McCallum2006; Wang, Zhai, and Roth 2013). However, few studieshave explored the problem of research trending topic pre-diction. Recently, (Prabhakaran et al. 2016) predict the riseand fall of a specific research topic using the temporal anal-ysis on the historical curve. However, they cannot preciselyproduce the research topic distribution by ignoring the cor-relation among different research topics. Besides, they donot provide a systematic study on the intrinsic mechanismsof research preferences formation either. These limitationshinder researchers from gaining deep insights into the futureresearch directions.

In each research area, there are often multiple top con-ferences promoting the research development jointly. Eachconference has its own research focus and interests. In themeanwhile, inevitably, the conferences belonging to thesame area (also called peer conferences) are mutually influ-enced. We take three top conferences, ’NIPS’, ’CVPR’ and’ACL’ in the Artificial Intelligence (AI) area, as an example.Figure 1 illustrates the topic evolution of the three confer-ences. In 2014, a number of papers published in CVPR provethe effectiveness of Convolutional Neural Network (CNN)in the image processing. The success of CNN inspires re-

RNN of conference i

research topic embedding

bag of words

RNN of conference j

RNN of conference k

Influence context

prediction

influence unit influence unit

Figure 2: The framework of Correlated Neural Influence Model.

searchers who work on the Natural Language Processing(NLP) problems. The CNN model, which barely gains theattention from NLPers before, rapidly becomes a hot topicdiscussed at ACL in 2015. Though several citation-basedindexes, such as h-index (Hirsch 2005) or g-index (Egghe2006), have been proposed to measure the overall impact ofeach conference, they cannot capture the cross-conferencescientific influence on the dynamics of research topics, asillustrated above.

In this paper, we focus on the study of research trend-ing topic prediction for mutually influenced conferences.We formulate this task as a multiple correlated sequencesprediction problem and propose a novel framework namedCorrelated Neural Influence Model as the solution. In theframework, we use a Recurrent Neural Network (RNN) (El-man 1990) to sequentially track the research topics for eachindividual conference by embedding all research topics intothe hidden space. The hidden states of RNN record the ac-cumulation of sequential historical research interests at dif-ferent time steps. Though RNN has been proved to have astrong ability to capture the sequential properties (Mikolovet al. 2010), it cannot uncover the cross-sequence depen-dencies caused by the mutual influence among conferences.To address this limitation, we propose a scientific influenceunit, which correlates the multiple RNNs. It is able to con-struct the influence context for each conference by integrat-ing the research hidden states of its peer conferences. Fromthe combined vector of the hidden state and the influencecontext of a conference, our model produce the distributionover all topical words in the next time step. In such a way, theproposed framework jointly models the sequential researchinterests and external cross-conference influence in trendingtopic prediction.

The contributions of this paper are as follows:

• To the best of our knowledge, we are the first to system-atically study the problem of research trending topic pre-

diction through exploring the scientific influence behindthe research evolution.

• Taking the scientific influence into account, we formulatethe problem as a multiple mutual-influenced sequencesprediction. We propose a novel framework, where multi-ple correlated RNN chains are developed for the researchevolution modeling.

• We conduct experiments on a scientific dataset includingpublications of top conferences in artificial intelligenceand data mining areas to demonstrate the effectivenessof the proposed model. We also demonstrate the inter-pretability and predictability of the proposed model bylooking into the two questions of most concern, includ-ing what the next rising trending topics are and for eachconference who its most influential peer is.

Correlated Neural Influence ModelThe research topics of a conference would change with theresearch development of the other conferences in the sameresearch area. The future research topics of a conferencecannot be predicted merely based on the past publicationsof its own. According to the statistics on the AI conferences,about 40% new emerging words come from the other con-ferences. This motivates us to propose a Correlated NeuralInfluence Model (CONI) that can integrate the scientific in-fluence of the peer conferences and jointly model the re-search evolution of all conferences in a unified framework.An overview of CONI is presented in Figure 2.

Problem FormulationC = {c1, c2, · · · , cn} represents the set of conferences ina research area. Let W = {w1, w2, ..., wv} be the set ofv research topical words appearing in C. We use the bag-of-word vector cti ∈ Rv to represent the research topicsof conference ci at the tth year. The jth location of the

bag-of-word representation is the normalized frequency ofthe word wj occurring in ci’s publications. Given the re-search topics of each conference at each year, we constructa sequence of research topics for each conference ci, i.e.,{c1i , c2i , · · · , cTi }.

The influence relationships are represented by an adja-cency matrix G ∈ Rn×n. The i-th row Gi∗ represents therelationship between ci and its peer conferences. We assumethat a conference ci can be influenced by all peer confer-ences in the same research area. Thus, for each ci ∈ C,Gij = 1 if i 6= j, otherwise Gij = 0.

Based on the notations above, the problem studied in thiswork is formulated as follows. Given the research topic se-quence of each conference ci before time step T and the in-fluence relationship matrix G, the objective is to predict thefuture research trending topics cT+1

i at the next time stepT + 1 for each conference.

Sequential Modeling of Research EvolutionIn order to track the research evolution in each conferenceand to explore its sequential properties, the proposed CONIextends the well-known RNN framework to model the re-search topic sequences. It takes the research topics at thecurrent time step as the input and iteratively encodes the re-search history into the hidden state to capture the researchinterests of the conference. Sequences of all conferences aremodeled by multiple RNN chains, which share the same pa-rameters. For ease of presentation, we take conference ci asan example to introduce how CONI sequentially updates itshidden state of research interests according to the historicalresearch topics.

The research topics of a conference at a specific timestep are represented as a distributed vector over all the top-ical words. However, this bag-of-word representation maycause the ’curse-of-dimensionality’ problem when the vo-cabulary size increases. To avoid this problem, we resort tothe word embedding techniques (Mikolov et al. 2013) andconvert the bag-of-word representation cti into a dense andlow-dimensional vector xt

i through an embedding projectionmatrix Φ ∈ Rdw×v . The embedding matrix Φ maps eachword wj into a dense vector Φj . The research topics of theconference ci at time step t are then represented by the vec-tor xt

i.xti = Φcti (1)

where xti ∈ Rdw .

Taking the research topics embedding xti as the input, the

hidden state hti is iteratively updated with its previous hid-

den state ht−1i , which is calculated as follows:

hti = f(ht−1

i ,xti) (2)

The recurrent function f has different variants, includingthe hyperbolic tangent function used in the standard RNNor other complex transformation functions like GRU (Choet al. 2014) and LSTM (Hochreiter and Schmidhuber 1997).In CONI, we utilize GRU for its effective performance andaffordable computation cost. The corresponding update for-mulas are:

hti = (1− zt

i)� ht−1i + zt

i � ht

i (3)

influence context

influence strength

interestedness

interested peer information

Figure 3: The scientific influence unit. The dotted lines areaimed to capture i’s interestedness on the research outcomesof its peers ht−1

j and ht−1k , which is denoted by Eq. 7. The

solid lines illustrate that after selecting the peer research out-comes with the interestedness, the interested peer informa-tion is combined under the influence strength αij and αik,forming the influence context, which is denoted by Eq. 8and 9.

ht

i = tanh(Wxti +U(rti � ht−1

i ) (4)

rti = σ(W rxti +U rh

t−1i ) (5)

zti = σ(W zx

ti +Uzh

t−1i ) (6)

where rti is the reset gate that controls to drop the part ofhistorical information irrelevant to the future trends. zt

i isthe update gate that controls whether to keep the new com-ing information in the hidden state ht

i. σ represents thesigmoid function. W ,W r, W z ∈ Rdh×dw and U ,U r,Uz ∈ Rdh×dh .

Scientific InfluenceIn addition to following the within-conference research in-terests, a conference also follows the research developmentof its peer conferences. Under the cross-conference influ-ence, the research interests of the conference will change ac-cordingly. The RNN can well capture the historical within-conference research interests through the hidden states, butit lacks the ability to discover the dependencies on thepeer conferences. In this paper, we correlate the sequencesof multiple conferences by constructing a compact influ-ence context, which combines the research information fromone’s peer conferences.

Two challenges arise when constructing the influencecontext vector. First, how to select the information a confer-ence is interested from its peer conferences? Second, howto distinguish the influence from different peer conferences?We address these challenges by introducing a scientific influ-ence unit, whose graphical illustration is presented in Figure2 and elaborated in Figure 3. It selects the interested infor-mation from peer research outcomes utilizing an attentionmechanism. It then linearly composites the interested infor-mation from all peer conferences based on the learned influ-ence strength into a compact vector.

Interested Information Selection For a conference, a va-riety of research topics are presented in its peer conferences.However, not all topics attract its attention. We design an at-tention mechanism to allow the research information that a

conference may be of interest to pass to it and meanwhilefilter out those uninterested. Often, a conference would liketo select the interested ones according to its current researchtopics. We expect the hidden state to be able to represent thehistorical research outcomes and thus measure the interest-edness based on the following calculation that involves bothone’s current topics embedding xt

i and the state ht−1j of its

peer cj .dtij = softmax(W insx

ti · h

t−1j ) (7)

where W ins ∈ Rdh×dw and the softmax function is usedto normalize the attention on each dimension. The interest-edness is represented as a normalized vector, where the di-mension with a higher value indicates the hidden feature re-ceives more attention.

The interested information that a conference receivesfrom each of its peer cj is obtained by

stij = dtij · h

t−1j (8)

Cross-Influence Composition A conference is inevitablymore influenced by some peer conferences than others. Tointerpret the cross-influence between one conference ci andeach of its peers in Gi, we aim at learning a influence pa-rameter vector Ai ∈ Rn, where αij ∈ Ai represents theinfluence strength ci receives from its peer Gij .

The influence context vector can be constructed with thelinear composition of the interested research informationunder the cross-conference influence strength, which is de-noted in Eq. 9:

mti =

N∑i

(Gijαij) ∗ stij (9)

In the output layer, given the hidden state hti, and the in-

fluence context vector mti, we use the softmax function to

output the predicted distribution of research topical wordsct+1i ∈ Rv for each conference at the next time step t + 1.

The hidden state and the influence context vector are con-catenated and fed to the softmax predictor as follows:

ct+1i = softmax(W o[h

ti;m

ti] + bo) (10)

where W o ∈ Rv×(dh+dh) and bo ∈ Rv .

LearningWe employ the generalization of multinomial logistic lossas the objective function, which minimizes the Kullback-Leibler divergence (Cover and Thomas 2012) between thepredicted topic word distribution ct+1

i and the target worddistribution ct+1

i :

L =

n∑i=1

T∑t=1

KL(ct+1i ||ct+1

i ) (11)

where

KL(ct+1i ||ct+1

i ) =∑j

ct+1i,j log

ct+1i,j

ct+1i,j

The model is trained by minimizing the loss for the re-search topic sequences of all conferences. Because the hid-den states are correlated through the scientific influence unit,we jointly conduct back-propagation along multiple chains(Alahi et al. 2016), and update the parameters with the Adam(Kingma and Ba 2014) algorithm.

ExperimentsDatasetWe select conferences from two active research areas in CS,i.e., Artificial Intelligence (AI) and Data Mining (DM), asexperimental objects to evaluate the proposed model on re-search trending topics prediction. In the AI area, 7 top con-ferences including AAAI, ACL, CVPR, ICCV, ICML, IJCAIand NIPS are selected, and in the DM area, 4 top confer-ences, i.e., KDD, ICDM, CIKM and SDM are considered.

We obtain the paper information of the above-mentionedconferences from a DBLP dataset published by (Tang et al.2008) and updated in 2016. This dataset collects around 3.2million research papers in CS since 1936. Each paper is as-sociated with its author, title, venue, publication year. Basedon the venue information, we recognize papers published onthe target conferences and extract the titles, venues and pub-lication years to constitute the experimental dataset.

Preprocessing The title is the most important element ofa scientific article and the main indication of article’s sub-ject and topic (Jamali and Nikzad 2011). Therefore, we usethe title as the topic summarization of each paper. For con-ference ci at year t, we treat the title words, which occurmore than once in conference publications of this year, asthe research trending topics. The statistics of the dataset ispresented in Table 1.

Table 1: Statistics of the dataset.area total papers topical word time periodAI 73071 3200 1995-2015

DM 9063 1562 2001-2015

Compared MethodsTo demonstrate the effectiveness of the proposed model,we conduct comparative experiments against the followingmethods, including one basic baseline, two popular influ-ence models, one time series model and two variants ofCONI.

• Baseline: It takes the research topics of all conferences atcurrent year as the predicted topics for each conference inthe next year.

ct+1i =

∑nj ctj

n

• Degroot model (DeGroot 1974): It is a classical influ-ence model, which learns the interpersonal influence byexploring the correlation between a user’s opinion and itsfriends’ opinions. Following the same idea, we model the

frequency of a topical word in each conference individu-ally as follows:

ct+1i,k = αiic

ti,k +

n∑j=1

Gijαijcti,k (12)

where αii and αij represent the self-influence and the in-fluence that conference i receives from conference j.

• Flocking model (Hegselmann and Krause 2002): It isa variant of the Degroot model assuming that a useronly trusts the friends who have similar opinions withher/himself. We calculate the research topic similaritiesbetween conferences with the Euclidean distance and con-struct the influence relationship matrix G, where Gij = 1if the distance between two conferences is less than 0.01.The research topics are predicted based on the Eq. 12 withthe created G.

• ARMA (Brockwell and Davis 2013): The autoregressivemoving average (ARMA) model is a widely-used time se-ries model, which has been successfully applied on cita-tion prediction (Yogatama et al. 2011). For each confer-ence, the frequency dynamics of each topical word at eachyear is regarded as the time series and the ARMA individ-ually predicts the frequency of each word at the next year.

• CONI I: It is an influence-free version of CONI. It usesthe GRU-based RNN to model the research evolutionof each conference and neglects the scientific influenceamong them.

• CONI V: It is another variant of CONI, which utilizesthe information from peer conferences directly instead ofmaking selection with the attention mechanism.

Model SettingsTo pre-train the initialized representations of topical wordsin the above-mentioned research areas. we construct a large-scale corpus by collecting papers in the areas of data mining,artificial intelligence, computational linguistics and com-puter vision from arxiv.org1, which is a repository of elec-tronic preprints of scientific papers. The corpus covers morethan 95% of the words occurring in the experimental dataset.Given the titles and abstracts of the collected papers (24833tokens), the word representations with the dimensionality of50 are trained using the continuous bag-of-words architec-ture (Mikolov et al. 2013). Words absent in the set of pre-trained embeddings are initialized randomly. We also setthe dimension of hidden state as 50 for CONI, CONI I andCONI V. The other parameters of each compared model areset for their best performances experimentally.

Evaluation MethodologyTo evaluate the prediction performance, we organize the re-search topics for each conference in temporal order. The first70% data is used for training, the following 15% data for thevalidation and the remaining 15% for testing. We adopt threemetrics. The metric Root Mean Squared Error (RMSE)

1https://arxiv.org/

Table 2: Performances of different methods.Area Methods MAP RMSE

AI

Baseline 0.5073 2.327e-3Degroot 0.1967 1.588e-3Flocking 0.1941 1.615e-3ARMA 0.2958 3.316e-2CONI I 0.5719 9.253e-4CONI V 0.5717 9.150e-4

CONI 0.5923 8.897e-4

DM

Baseline 0.4920 3.785e-3Degroot 0.3263 2.28e-2Flocking 0.3155 2.46e-3ARMA 0.2958 3.32 e-3CONI I 0.5024 1.700e-3CONI V 0.5000 1.731e-3

CONI 0.5128 1.610e-3

measures the precision of the predicted word frequencies.Another two metrics, i.e., MAP and NDCG@K, are moreconcerned with the ranking of the predicted words.

Performance EvaluationThe prediction performances of all methods in terms ofMAP and RMSE are shown in Table 2. Overall, CONIachieves higher performances than all compared methods inboth research areas. Based one the results, some importantfindings are concluded as follows.

RNN better captures sequential properties. The RNN-based methods including CONI I, CONI V and CONI sig-nificantly outperform the time series model ARMA. Itdemonstrate that RNN has better ability to capture the se-quential properties of the research evolution than ARMA,since RNN models the dynamics of all research topics wordsglobally instead of individually modeling the time series ofeach topical word.

Cross-conference influence is important. The betterperformances of CONI than CONI I support our assumptionthat the research development of a conference is truly influ-enced by its peer conferences. Besides, we also observe thatboth influence models Degroot and Flocking have poor per-formances, which denotes that the influence mechanisms foropinion formation cannot be applied to model the researchevolution of mutually influenced conferences.

Attention mechanism benefits. The performance ofCONI V, which does not use the attention mechanism toselect the interested information from peer conferences isworse than CONI, and even does not show a significant im-provement compared with CONI I. It reveals that the pro-posed attention mechanism is able to effectively capture themost relevant information that influences a conferences fu-ture research.

CONI works effectively on top topic prediction. For amore detailed analysis, we also evaluate the performancesof models on top K topical word prediction using the rank-ing metric NDCG@K. The results are illustrated in Figure 4.CONI again achieves the best results, and the improvementsare more significant when K is smaller than 30 in both re-

Figure 4: Performances on NDCG@K

search areas. It reveals that CONI has a stronger ability inpredicting top trending words.

DiscussionWhat are the next rising trending topics? Providing in-sights into the topics that have the potential to become trend-ing topics in the future is also important for researchers tocatch up with the rapid progress of research. We use the pub-lications before 2015 to predict the trending topical words in2016. Among all the words, we select 10 words that increasethe most significantly from 2015 to 2016 in terms of theirrankings. The results are presented in Table 3.

Since the publications in 2016 are not included in thedataset, we check the predicted rising trending topical wordswith the publication records on the conference website.For instance, ’reinforcement’, which denotes reinforcementlearning is predicted as the fastest rising trending topic inICML in 2016. According to the publication records ofICML, the percentage of publications related to ’reinforce-ment’ increases to 8/322 in 2016 compared with 3/270 in2015. In 2017, the percentage of papers working on ’re-inforcement’ even increases up to 21/466. With the pre-dicted rising topics, researchers could change to explore newpromising research topics in their future studies.

Who is the most influential peer? With the proposedmodel, the scientific influence strengths among conferencesare learned from the correlation of their research topics, andare denoted by the parameter Ai∗ for each conference ci.We normalize the learned influence vector Ai∗ and plot it asa row in Figure 5, which represents the influence strength cireceives from each of its peers.

From Figure 5, a lot of interesting findings about the con-ference community in AI and DM areas can be observed. Forexample, we find that the ACL conference which focuseson the natural language processing is greatly influenced bythe conference CVPR in the computer vision area, which isconsistent with the example shown in Figure 1. Meanwhile,ACL is among the top 3 influential peers for CVPR. It indi-cates that the research communities in the computer vision

and natural language processing have strong mutual influ-ence. Besides the above finding, more observations aboutthe research community can be inferred from the figure, andwe do not discuss in this paper due to the space limitations.

Related WorkResearch Trend AnalysisIn the camp of bibliometrics and scientometrics, researchershave been studying how to discover the emerging researchtopics for a long time. They treat the scientific literature asthe citation networks and proposed different methods basedon the citation network analysis (Shibata et al. 2008) or tem-poral citation patterns (Daim et al. 2006; Small 2006) to dis-cover which research area will rapidly grows. In contrast tothis line of work, our focus is not on the citations, but onpredicting the research topics reflected from the text of thepublications.

There is another branch of research that tries to under-stand the trending topics in scientific papers from the text in-formation. Existing models of this branch are mainly basedon the topic model, i.e., LDA (Blei, Ng, and Jordan 2003),which is initially applied to statically detect different re-search topics from corpus of scientific papers (Griffiths andSteyvers 2004; Steyvers et al. 2004). Considering the topicdynamics over time, these dynamic topic models are de-veloped to discover when and where the topics evolve andcapture the dependencies among different topics (Wang andMcCallum 2006; Wang, Zhai, and Roth 2013). However,these models lack the ability of predicting future trendingtopics since they only focus on exploring how the content ofa topic forms and changes over time. Recently, Prabhakaranet al., (2016) proposed that the rhetorical roles that authorsascribe to topics (as methods, as goals, as results,etc.) canbe the clues of topic trends. They combined the topic modelwith rhetorical role analysis to predict the rise and the fallof each detected topic. Although the model is able to pre-dict the trends, it cannot precisely produce the popularity ofall topics by ignoring the correlation among different topics.Different from the above work, our proposed model globally

Table 3: Predicted 2016 fast-rising trending topicsAAAI generative, intelligent, spatial, translation, kernel, relation, function, extraction, robotic, reasoningACL sense, discourse, dialogue, web, spoken, bayesian, interactive, temporal, induction, syntactic

CVPR field, memory, recurrent, illumination, recovery, surface, training, shapes, bayesian, filteringICCV natural, space, optimal, photometric, probabilistic, mixture, representations, group, knowledge, geometricICML reinforcement, decision, active, kernel, online, prediction, temporal, structure, theory, recurrentIJCAI boosting, alignment,nonlinear, latent, markov, relational, regularization, active, bounds, treeNIPS regression, clustering, application, hierarchical, performance, representation, generalization, combine, knowledge, naturalKDD feature, pattern, contextual, ranking, trust, ensemble, extraction, sequential, local, latentCIKM distributed, prediction, ensemble, selection, large, database, processing, set, support, applicationICDM ensemble, pattern, contextual, graph, multiple, dynamic, retrieval, compression, similarity, streamSDM weighted, dimension, kernel, detection, ensemble, efficient, regression, knowledge, analysis, nearest

stands for how much NIPS is influenced by CVPRLegend: NIPS

CVPR

Figure 5: Who is the most influential peer?

tracks the evolution of all research topics and can be directlyapplied to trending topics prediction.

Influence ModelingMeasuring the scientific influence is very important to al-locate the efforts and resources in science. Several indexesbased on the citation records such as h-index (Hirsch 2005)or g-index (Egghe 2006) are proposed, and have been widelyused to estimate the impact of an author or a paper over thewhole research community. However, this macro view canhardly provide deep understanding on the detailed effect ofscientific influence. Furthermore, several studies were de-veloped to detect the scientific influence of articles fromthe citation data and textual content (Shen et al. 2016;Foulds and Smyth 2013). However, their studies did not at-tempt to explore on the the future research trending topicprediction by employing the detected scientific influencewhich is the focus of this paper.

On the other hand, the effects of influence in the opin-ion prediction has been well studied. Most popular workformulates each user’s opinion as a digit and studies thecorrelation between a person’s future opinion and her/hisfriends’ opinions through learning the interpersonal influ-

ence. Several models with different influence assumptionsare proposed (DeGroot 1974; Hegselmann and Krause 2002;Chen et al. 2016). Although these models achieve goodperformances on predicting personal opinion behaviors, theidea has never been applied to reveal the influence mecha-nisms among scientific conferences.

Conclusion and Future Work

In this paper, we propose a correlated neural influence modelto study the intrinsic mechanism behind the research evolu-tion of conferences and predict their trending research top-ics. It has the ability to capture the sequential properties ofthe research evolution and correlate different conferencesunder the cross-conference influence. We demonstrate theeffectiveness of the proposed model by conducting experi-ments on a scientific dataset and our proposed model showsthe best results compared with state-of-the-art methods. Inthe future, we will categorize the topical words with dif-ferent aspects, e.g., algorithm, application, and capture thecross-conference influence in the aspect-level for providingdeeper understandings on the research evolution.

AcknowledgementsThe work in this paper was supported by ResearchGrants Council of Hong Kong (PolyU 5202/12E, PolyU152094/14E), National Natural Science Foundation of China(61272291) and The Hong Kong Polytechnic University (4-BCB5, G-YBJP).

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