Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018)

On Group Prediction in Event-Based Social Networks

Guangyu Li,1 Yong Liu,1 Bruno Ribeiro,2 Hao Ding1 1The Department of Electrical and Computer Engineering, New York University, Brooklyn, NY 2The Department of Computer Science, Purdue University, West Lafayette, IN guangyu.li, yongliu, [email protected], [email protected]

Abstract user-level features related to user’s attention: how active is a user in individual groups, and how does the user distribute Although previous work has shown that member and struc- her activity/attention among multiple groups. Based on these tural features are important to the future popularity of groups features, the first key idea of our approach is the use of role in EBSN, it is not yet clear how different member roles and the interplay between them contribute to group popularity. discovery to determine the importance of users in a group In this paper, we study a real-world dataset from Meetup and the roles they play. Specifically, armed with each group — a popular EBSN platform — and propose a deep neu- member’s role, and event co-participation graphs generated ral network based method to predict the popularity of new from those members’ activities, we combine the members’ Meetup groups. Our method uses group-level features spe- roles with these activity networks to predict a group’s suc- cific to event-based social networks, such as time and loca- cess, extending the fingerprints techniques developed to cor- tion of events in a group, as well as the structural features relate the characteristics of atoms along with the neighbor- internal to a group, such as the inferred member roles in a ing bonds to atoms to determine a molecule’s function. group and social substructures among members. Empirically, Our extension also accounts for a user’s limited attention by our approach reduces the RMSE of the popularity prediction incorporating “attention-based” features, such as how many (measured in RSVPs) of a group’s future events by up to 12%, against the state-of-the-art baselines. groups a user joined and how much time the user spent in each group into the member-level feature .

Introduction Related Work As online social networks become more prevalent, peo- There are two major lines of research for this problem. One ple’s face-to-face interactions are reshaped by these net- focuses on characterizing the evolution of online social net- works. In this work we focus on event-based social net- work popularity by applying mean-field epidemic models works (EBSN), online social networks whose members hold to the time series of the “daily active user”, without user- in-person events. Meetup (Meetup.com 2018) is a popular level or network structural (Ribeiro 2014). The EBSN that allows its members to find and join online in- other focuses on using general group features to make pre- terest groups, and organize face-to-face events in different dictions (Liu and Suel 2016; Qiu et al. 2016). In contrast, categories, such as , books, games, movies, health, our approach uses richer information and convolutes mem- pets, careers, and hobbies, etc. ber roles, member’s attention-capacity features, with their In this paper, we study the problem of group popularity activity network structure to achieve higher prediction accu- prediction in EBSN, with a special focus on new groups. racies. More specifically, we focus on predicting the popularity (measured in the number of RSVPs) of newly established Proposed Method interest groups in Meetup. The main questions that we want to answer are: 1). can we predict the future success of new In this section, we first define a metric of a group’s popu- groups? 2). what are the observable factors that best predict larity and describe our prediction problem in the context of a group’s success? the Meetup EBSN. We then propose our prediction method, leveraging on the group-level features and member-level Contributions We develop a novel approach to predicting features. Finally, we present our overall method combining the popularity of newly formed groups in EBSN, achieving these group-level and member-level features to make predic- the state-of-the-art accuracy. Our approach considers vari- tions. ous factors: i) the group-level features, such as the past pop- ularity of the group and the number of events; ii) the event- Meetup Group Popularity Prediction based features, such as location and schedule of events; iii) Meetup is an event-based in which users can Copyright © 2018, Association for the Advancement of Artificial form and join different interest groups online, and orga- Intelligence (www.aaai.org). All rights reserved. nize and participate in face-to-face social events offline. The

644 group organizers create events, and each event has specified Radius 0: time, location and topic. The information about new events will be sent to group members through emails or website notifications. Each group member decides whether she will Radius 1: participate in the new events based on her time, location, and topic preferences, and then responds by sending RSVPs …. (“yes”, “no”, or “maybe”). With the definitions of groups, events and users in Meetup, the group popularity prediction Radius 2: problem can be defined as: …. Definition 1. Given the activities of a group within a time window of [0,n] months, and a time interval of m months, predict the group’s total RSVP number (popularity) within a Circular Fingerprints: Identifier0 Identifier1 Identifier2 Identifier3 …….. future time window of [n + m, 2n + m] months. Figure 1: Subgraphs Detected by Circular Fingerprints in The time interval of m months can be chosen to eliminate Social Network. Circular Fingerprint scans the network for the effect of seasonal event holding patterns. all subgraphs under certain radius. Each subgraph is then encoded into an integer identifier. Integer identifiers of all Group-level Features subgraphs constitute the circular fingerprints. The most straight-forward features to use for popularity pre- diction are the summary statistics of each group. So we start with extracting “group-level features”, which are vari- adjacency matrix adjacency matrix

ous summary statistics of a group without examining the de- Layer 0 Layer 1 ࢘ ૙ ૚ ࢇ ࢘ࢇ tailed features of each member in the group. We list the de- ૙ ࣌ሺȉ ࡴ ሻ ࣌ሺȉ ࡴ૚ሻ ……. ૙ scriptions of fourteen group-level features for each Meetup ࢘ࢇ ൅ ࢘ ૚ ൅ ૙ ࢇ ࢘ ሺ࢔ࢋ࢏ࢍࢎ࢈࢕࢙࢘ሺࢇሻሻ ࢘૚ሺ࢔ࢋ࢏ࢍࢎ࢈࢕࢙࢘ሺࢇሻሻ group in Table 1, such as the scheduled time distributions ૙ ૚ of its events, the location distributions over its venues, and ࢙࢕ࢌ࢚࢓ࢇ࢞ሺȉ ࢃ ሻ ࢙࢕ࢌ࢚࢓ࢇ࢞ሺȉ ࢃ ሻ RSVP counts of all members, etc. ࢌ૙ ࢌ૚ Internal Group Features ڮ +ࢌൌࢌ૙+ࢌ૚ Internal features of a group can be defined as all the fea- tures that are related to each individual member in the group. These features should include the first-order features that can be directly calculated using basic statistics, such as the Figure 2: Group-Role Neural Fingerprints Algorithm. “Fea- past attendances of a member and how many groups a mem- ture Update” operation: each member’s feature is updated ber has joined. They should also include the second-order so that the 1-hop neighbors’ features and neighboring edge features that require further processing, such as the member features are added to the initial member feature. role discovery and the structural feature extraction. Member-level Feature Extraction We start with con- Circular Fingerprints In order to extract structural fea- structing social graph for each group from which the fea- tures embedded with member roles, we use the “circular tures are extracted. Based on the event co-participation so- fingerprints” algorithm (Rogers and Hahn 2010). Circular cial graph, We propose twelve member-level features listed fingerprints is a popular tool for handling graph-structured in Table 1: Feature m1∼m6 represent “who you are”, i.e., data in chemistry. In the context of event-based social net- the features related to the member’s own characteristics, and work, we draw the analogy between a molecule and a group. feature m7∼m12 represent “who you know”, i.e., the fea- We assume the members of a group are analogous to the tures related to the characteristics of her neighbors in the atoms of a molecule, and the social ties between members group’s social graph. are analogous to the chemical bonds (Figure 1). Then we can study how the subgraphs between members contribute Member Role Discovery To find role features of each to the group popularity using the circular fingerprints frame- group member, we use Non-negative Matrix Factorization work. The identifier list (also called “fingerprints”) is then (NMF) (Lee and Seung 2001). used to characterize the properties of the molecule. In our Taking the member-role sub-matrix Mg for group g gen- case, we use the fingerprints as structural features to predict erated by the role discovery method, we sum over all the the group’s popularity. members (rows) and get the group’s role distribution vec-  Group-Role Neural Fingerprints Overcoming some lim- Vg = Vg = n Mg , 1 ≤ j ≤ r tor j i=1 ij , then we stack itations, Duvenaud et al. (Duvenaud et al. 2015) proposed all the vectors {Vg} to form a group-role matrix Ω ∈ Rp×r a convolutional neural network, where each neural network where p represents the number of groups and r is the number layer simulates the updating operation in circular finger- of roles. prints (Figure 2). We now extend the convolutional finger-

645 epit ewr ihtoMliae ecprn (MLPs). Perceptrons fin- Multilayer and neural two our group-level with of network combine combination gerprints to a is used It is features. member-level network neural deep Features a Internal and Group-level RAM. Combining 96G and CPU single-thread 2.67Hz contains with computer which batch training each oefaue is features depth role of fingerprint length neural Group-role the Analysis Cost more Computational captured. iteration, are each information After structural iterates reached. local algorithm more is and the radius way, certain this a until In row matrix. corresponding neighbors’ the member-role to 1-hop of added member’s are each vectors member-role that distribution role so the up- updated operation, “feature is update a matrix this through is In output matrix operation. the adjacency date” direction, the other with the as in updated calculated fingerprint; di- directly 0 two is in radius output goes the the layer direction this one of output in the rections: then discovery input, role the previous as the step by member- produced initial is the which takes matrix network role the in layer hidden first the ie of of sizes length fingerprint with GRNF a rn loih osleorpolm ednt hsap- this denote We problem. our the as solve proach to algorithm print sn h etpsdtstAIfo t est,we website, New its of miles from 50 within API located groups dataset Meetup all Meetup’s crawled the Using Description Dataset Group-level features 1.SmRV ubro atevents. past past of number of RSVP Sum numbers g14. RSVP events of Variance events past g13. all of number held RSVP Average has g12. group the graph number Event social group’s g11. the of degree graph Total social g10. group’s the of Density held g9. are over event distribution the venues location all the of Entropy g8. dis- “member-member” tances the of group Variance same the g7. member in member any other any between and distance Average g6. distances “event-member” the of Variance g5. and member event participating any any between distance Average g4. distances “event-event” the of Variance g3. held events group two the any between distance Average all g2. held over are distribution events time the times the of Entropy g1. L (10 fasca rp with graph social a of × ru-oeNua Fingerprints Neural Group-Role efrac Evaluation Performance 10 O × ( N L RNF 10) ae nteodro n iuefor minute one of order the on takes + RNF N h optto otfor cost computation The oe hthave that nodes 10 2 100 ) n ovlto layer convolution and npatc,training practice, In . al :GoplvladMme-ee Features Member-level and Group-level 1: Table oilgah na on graphs social R o ais0, radius For . fingerprint , F euse We related Member-level features itiuinoe h ruste joined they groups fraction the event over neighbors’” distribution “1-hop of entropy Average joined m12. they number groups event the over neighbors’” distribution “1-hop of entropy joined Average have m11. they groups the over dis- havetribution attendance neighbors’” “1-hop neighbors” of entropy Average “1-hop m10. the number joined group Average m9. par- have neighbors” “1-hop ticipated the number neighbors event Average 1-hop member’s m8. the of groups degree Average the m7. over distribution joined member fraction the event of groups Entropy the m6. over distribution joined number member the event of Entropy the m5. over joined distribution member attendance the groups member’s of joined Entropy has m4. member the current number Group in m3. participated has member group the number Event member m2. the of degree Total m1. 646 leigottegop ihu ai nomto ocalcu- to information After valid accuracy. Er- without groups prediction Squared the RSVP Mean out measure Root filtering ten to the to (RMSE) use month We ror the numbers. one against RSVP from tested are true ranging fu- numbers RSVP the interval predicted in The time months months. a num- three held RSVP of after group the window ture of time formed prediction another newly make within then a months ber We three when event. first first time exper- its the our the In from from year. extracted starting each are of in groups features future groups all predicting iments, new on include of focus which numbers only 2016), we RSVP ages, al. and et sizes (Qiu Suel different and (Liu and by 2016) introduced settings experiment the Unlike Prediction Popularity Group ldn l h eae eadt.Tbe2smaie the summarizes dataset. 2 collected Table the in- of meta-data. 2015, statistics related salient February the to 2003 all March cluding from (NYC), City York v.Eet e cieue 9.38 72.26 5.67 user active per Events Avg. 3.54 93,643 event per Participants Avg. 8,338,382 group 274.13 per Events 1,025,719 Avg. joins user 1,101,336 a Groups Avg. group per Members Avg. 17,234 venues of Number RSVPs of Number events of Number users of Number groups of Number Name al :DtstStatistics Dataset 2: Table Horizon Baseline 1 Baseline 2 Proposed Gain over Best (months) (best perf.) (best perf.) Baseline 3 Method Baseline (p-value) 0–3 83.92 94.61 103.53 74.78 10.90% (< 0.012) 1–4 89.85 101.31 105.44 80.32 10.61% (< 0.012) 2–5 99.81 107.45 107.64 88.62 10.68% (< 0.015) 3–6 106.15 113.11 108.71 95.96 8.53% (< 0.022) 4–7 103.91 108.74 108.61 99.84 3.91% (< 0.021) 5–8 117.58 111.13 108.32 96.69 10.73% (< 0.012) 6–9 114.38 117.28 117.28 99.85 12.69% (< 0.009) 7–10 126.52 139.67 126.41 110.83 12.32% (< 0.011) 8–11 133.41 150.28 138.61 118.07 11.51% (< 0.014) 9–12 137.75 139.36 140.54 122.04 11.39% (< 0.014) 10–13 143.83 151.24 153.67 126.86 11.42% (< 0.011)

Table 3: Prediction Accuracy (RMSE) Comparison with Baseline Methods late the features, we have more than 7,000 new groups along features. We also demonstrated that member roles and inter- with their features and RSVP numbers. We randomly choose action among members with different roles, characterized by 6,000 groups and perform five-fold cross-validation to se- neural fingerprints, can better represent the intrinsic member lect the best hyper-parameters used in all the baselines and behaviors and the of a group than the raw GRNF algorithm. member features and the activity graph. Comparison with Baseline Methods We compare our References method which uses both group-level features and internal features (shown in Table 1) with three competitive baselines: Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; and Adams, R. P. 2015. • Baseline 1 (Liu and Suel 2016): in addition to meta in- Convolutional networks on graphs for learning molecular formation about the groups, it also uses the averaged fingerprints. In Advances in neural information processing member-level features, such as “average event attendance systems, 2224–2232. of members” and “standard deviation of event attendance of members” etc. Lee, D. D., and Seung, H. S. 2001. Algorithms for non- negative matrix factorization. In Advances in neural infor- • Baseline 2 (Qiu et al. 2016): it demonstrates that structural mation processing systems, 556–562. features like triads counts and clustering coefficients have Liu, X., and Suel, T. 2016. What makes a group fail: Model- strong predictive power for predicting the longevity of the ing social group behavior in event-based social networks. In group’s lifecycle in an online social messaging network. Big Data (Big Data), 2016 IEEE International Conference • Baseline 3 (Ribeiro 2014): it uses epidemic model of dif- on, 951–956. IEEE. ferential equations to fit the evolution curve of group’s Meetup.com. 2018. Meetup.com — Wikipedia, the free popularity. One advantage of this model is that it provides encyclopedia. [Online; accessed 19-January-2018]. decent accuracy by only using the time series of daily ac- tive users (DAU). Qiu, J.; Li, Y.; Tang, J.; Lu, Z.; Ye, H.; Chen, B.; Yang, Q.; and Hopcroft, J. E. 2016. The lifecycle and cascade of The results in Table 3 show that our final proposed wechat social messaging groups. In Proceedings of the 25th approach clearly outperforms all baselines in all predic- International Conference on World Wide Web, 311–320. In- tion horizons, ranging from predicting the average 3-month ternational World Wide Web Conferences Steering Commit- RSVP numbers in the immediate next three months to pre- tee. dicting this quantity ten months after the last record in the Ribeiro, B. 2014. Modeling and predicting the growth and training data. As expected, for all methods, the error of pre- death of membership-based websites. In Proceedings of the dicting nearer future is smaller. Thus, it is important to con- 23rd international conference on World Wide Web, 653–664. trast the accuracy gains of our method against all baselines, ACM. which range from 3.91% to 12.32%. Rogers, D., and Hahn, M. 2010. Extended-connectivity fin- Conclusion gerprints. Journal of chemical information and modeling 50(5):742–754. In this paper, we proposed a deep neural network method to predict the future popularity of groups in event-based social networks. Our method outperformed all the state-of-the-art methods. Along the way, we have analyzed a few key fac- tors contributing the most to these predictions. Specifically, we showed that location and time are important group-level

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