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Community-Based Event Detection in Temporal Networks


We propose a method for detecting large events based on the structure of temporal communication networks. Our method is motivated by findings that viral information spreading has distinct diffusion patterns with respect to community structure. Namely, we hypothesize that global events trigger viral information cascades that easily cross community boundaries and can thus be detected by monitoring intra- and inter-community communications. By comparing the amount of communication within and across communities, we show that it is possible to detect events, even when they do not trigger a significantly larger communication volume. We demonstrate the effectiveness of our method using two examples—the email communication network of Enron and the Twitter communication network during the Boston Marathon bombing.


Event detection is of crucial importance in many socio-technical systems because events often bear anomalous outcomes of societal interest1, which range from unauthorized activities in computer networks2, fraudulent credit card transactions3 and disease outbreaks4. Most events of interest occur in networked systems, such as an organization, the society, or the Internet. Therefore identifying events in temporal networks has attracted much attention5. A key challenge in event detection is distinguishing events from natural system variations. Consider the case of email exchanges in an organization. An unusual volume of emails may not necessarily represent an event, but reflect seasonal behaviors. Communication traffic tends to vary based on particular dates (e.g., due to upcoming releases). Such variations represent a regular pattern of the email communication network and should not be associated to events6.

Traditional event detection methods focus on identifying changes in structural features at the macro- and microscopic level (e.g., in the distribution of the degrees of all nodes or in node properties like centrality measures)7,8,9,10,11,12,13,14. Model-based approaches combine block models with Bayesian change point detection15,16. More recent approaches analyze meso-scopic properties shared by nodes that are grouped into densely connected communities15,16,17. Simple approaches detect communities at particular time slices evaluate whether significant changes in the community structure at subsequent slices take place17. A key advantage of community-based methods is the robustness to fluctuations in link density18,19.

Here, instead of monitoring changes in the community structure itself, we propose to examine the difference between the ratio of inter- and the intra-community communication, supported by a previous finding that link information diffusion patterns with respect to communities to virality of the information20. The proposed method is likely to be less computationally expensive compared to other community-based methods since it does not require computing the similarity between communities of two networks every time slice.

Figure 1 illustrates the main idea of the method. When there is no global event, communication between nodes takes place mostly within each community (as in Fig. 1(a)). However, when a global event occurs, it spreads virally, crossing community boundaries and producing more inter-community communication (as illustrated in Fig. 1(b)). The proposed method detects such global events by monitoring the communication volume within and across communities. We demonstrate the effectiveness of the method by analyzing the email communication network of Enron (based on events reported in previous studies21,22) and the interactions between Twitter users during the Boston Marathon bombing.

Figure 1

Schematic representation of the proposed event detection method. For both networks nodes are associated to the same communities but different patterns of communication within and across communities emerge. (a) When there is no event, most communication takes place within communities. (b) When a large event occurs, more communication takes place across communities because of the global relevance and the virality of the event.

It has been shown that many types of information in society spread like complex contagions, i.e., successful transmission depends upon interaction with multiple carriers23. However, as a previous study demonstrates20, events of global interest tend to propagate as simple contagions, where the impact of reinforcement is weak. As a result, viral information cross community boundaries easily. We build on this observation by hypothesizing that systematic major events trigger large viral cascades. For the two case studies, the email communication network and the Twitter network, the related content about the events becomes easily accessible to multiple communities.



Using the email data from Enron, we compare the proposed method against the baseline of email volume. Figure 2 shows the time series of the volume of emails sent between 2000-09-30 and 2002-04-30. First, we consider whether the volume of emails correlates with the events associated to Enron’s collapse (depicted by the dashed vertical lines). Figure 2(a) shows the weekly volume of emails. The horizontal solid line represents the moving average of emails during the observation period using a window length equivalent to a year of data (52 weeks). Each horizontal red band represents one moving standard deviation from the moving average using the same window length (more intense bands indicate observations that are further away from the mean, based on Algorithm 1). Note that events 1, 4, 5 and 6 lie more than one standard deviation away from the moving average and their occurrence coincides with a burst of emails. However, this relationship does not hold for events 2, 3 and 7.

Figure 2

Time series of Enron events. (a) Time series of the number of emails. (b) Time series of the difference between the inter- and intra-community link ratios. (c) Time series of the number of emails classified by topics. (d) Time series of the difference between the inter- and intra-community link ratio classified by topics.

We measure the difference between inter- and intra-community link ratios detailed in equations (1) and (2). For the Enron dataset, the community partition results from a period of \({m}_{0}=91\) weeks. For discussion on selecting the appropriate value of m0, see Supplementary Information (SI), Section S1. Figure 2(b) shows that the occurrence of events, 1 through 7, coincide with peaks in the proposed measure. Even events that do not occur during periods of elevated volume of emails are associated with increased inter-community communication. This result supports our hypothesis that there is a considerable transmission of information through inter-community links when events take place. Note that the activity signal may occur before or after the events. It is natural to expect heightened activity before the “event” in many cases. The “events” in the Enron’s case are the public release of certain information. Therefore, it is reasonable to assume that, in some cases, such information had been circulating internally, preceding the actual “event.”

Figure 2(c) shows the volume classified into six topics24. One topic represents contents associated to daily activities; the remaining ones are associated to Enron’s bankruptcy. Most emails are classified into day-to-day activities. For the categories not related to Enron’s bankruptcy, there is no association between events and topics, suggesting that the volume of emails does not help us to characterize a detection pattern.

Figure 2(d) shows the difference between inter- and intra-community link ratios distinguished by topics. Note the association between events and topics. In particular, emails about day-to-day activities have a similar inter- and intra-community diffusion pattern during the entire observation period, depicted by the flat curve. For topics related to “utility companies difficulties,” “Federal Energy Regulatory Commission” and “wire stories about Enron’s demise,” there is a positive association. In other words, topics that are sensitive to bankruptcy diffuse across communities. The co-occurrence of peaks and events in Fig. 2(b) shows that the proposed criterion can be used as a signature for detection.

Figures 3 and 4 show the performance of detecting events using the proposed criterion against the volume of emails (for different detection resolutions). The detection resolution, denoted by m, describes the number of weeks within which we compute the output of the detection algorithm. We compare detection algorithms using the ROC25 and the PRC26 to take into account that the dataset is unbalanced27. The proposed approach performs generally better than volume-based detection, with noticeable improvements at lower resolutions. For \(m=7\), the proposed method has a perfect performance.

Figure 3

Performance comparison for the Enron case when \(m=2\) weeks. (a) ROC. (b) PRC.

Figure 4

Performance comparison for the Enron case when \(m=7\) weeks. (a) ROC. (b) PRC.

Boston marathon

Mention network

As in the previous section, we evaluate whether the communication volume relates to events associated with the Boston Marathon bombing (depicted by the dashed vertical lines and numbers). Figure 5(a) shows the hourly number of English mentions during April 2013. We use the same visualization conventions as in Fig. 2(a,b). Here the window length of the moving average is equivalent to four days of data (96 samples). Note that on 2013-04-08 at 07:00 UTC, there are some observations that fall three standard deviations beyond the mean. However, these observations are not associated with the events of interest. Similarly, on 2013-04-18, there is a significant decrease in the number of mentions due to missing data. Note also that around events 1 and 2 the number of mentions is comparable to the one in other hours during the observation period, i.e., these data points are statistically insignificant.

Figure 5

Time series analysis of the mention network. (a) Time series of the number of mentions. (b) Time series of the difference between the inter- and intra-community link ratios. (c) Distribution of the number of hashtags based on the total number of links (horizontal axis) and the difference of inter- and intra-community links (vertical axis) during the interval 14:00-16:00 EST on 2013-04-15. (d) Same as (c) during the interval 16:00-18:00 EST. (e) Same as (c) during the interval 18:00-20:00 EST. Hashtags related to the Boston Marathon bombing are highlighted.

Figure 5(b) shows the difference between inter- and intra-community link ratios which demonstrates significant deviation at the time of the events (for \({m}_{0}=7\) days). For event 1, the difference moves beyond four standard deviations—suggesting a significant increased in inter- compared to intra-community communications. For event 2, the difference between the ratios is three standard deviations, which is still noticeable compared to other times during the observation period. Figure 5(b) also shows other significant deviations. In particular, on 2013-04-21 at 17:00 UTC and 18:00 UTC, the proposed measure falls three standard deviations from the average. This behavior coincides with the hacking of the Associated Press Twitter account on 2013-04-21 around 17:00 UTC. A fake message reported that there had been “two explosions in the white house and Barack Obama [was] injured,” which caused financial markets to panic for a few minutes28.

We also analyze the contents of the mentions. Figure 5(c–e) show the distribution of the activity of each hashtag mentioned on 2013-04-15 at different time intervals after the bombing (in EST). In particular, we measure the total number of communications and the difference of the two modalities of communication (inter- and intra communication links). The red cells around the origin indicate that most hashtags are not frequently used. Note also that hashtags tend to be confined inside communities. This is evidenced by the absence of observations with large difference between inter- and intra-communication. Right after the bombing (at 15:00 EST) there are no hashtags with significant difference in the inter- and intra-community level in Fig. 5(c). However, in the two subsequent two hour intervals, hashtags related to the bombing emerged distinguished by lots of inter-community communications (see Fig. 5(d,e)). The highlighted cells correspond to the bombing related hashtags #prayforboston, #BostonMarathon, #PrayForBoston, #Boston and #BostonMararthon. These results demonstrate that the increase in the difference between inter- and intra-community communication is indeed triggered and driven by the bombing event.

Retweet network

Figure 6(a) shows the hourly number of English retweets. Note that on 2013-04-08 at 07:00 UTC, there are observations that lay up to three standard deviations from the mean, but are not associated with the events of interest. The spike in the retweet activity is before the bombing, which does not relate to the bombing event.

Figure 6

Time series analysis of the retweet network. We perform the same analysis as for the mention network and report the number of retweets in (a), the difference between inter- and intra-community link ratios in (b), the distribution of the number of hashtags based on the total number of links (horizontal axis); and the difference of inter- and intra-community links (vertical axis) during the interval 14:00-16:00 EST on 2013-04-15 in (c). (d) Same as (c) during the interval 16:00-18:00 EST. (e) Same as (c) during the interval 18:00-20:00 EST. Hashtags related to the Boston Marathon bombing are highlighted.

Figure 6(b) shows the difference between inter- and intra-community link ratios (\({m}_{0}=7\) days). For event 1, the difference between community ratios spiked up to four standard deviations, in accordance with Fig. 5(b). For event 2, the difference between community ratios is not as significant as for the case of mentions in Fig. 5(b). Other relevant deviations are not observed during the period.

We also explore the content diffused in the retweet network during the hours of the bombing. We measure the distribution of the number of links for each hashtag retweeted on 2013-04-15 at the same time intervals used for Fig. 5(c–e) and reported them in Fig. 6(c–e). For the retweet network, we do not observe a significant distribution of hashtags in the vertical axis after the bombing event (see Fig. 6(c)). However, Figs 5(d,e) and 6(d,e) show that the bombing related hashtags are placed in regions of low density but with significant difference in inter- and intra-communication links. These hashtags correspond to #prayforboston, #PrayForBoston and #BostonMarathon.


This paper demonstrates a proof-of-concept of a community-based method to detect the occurrence of global events in temporal networks. In doing so, we put forward a novel, theoretically grounded approach toward systematic event detection. We apply the proposed method to (i) the Enron email dataset (during its collapse period); and (ii) the Twitter mention and retweet dataset (when the Boston Marathon bombing took place). We hypothesize that events like Enron’s bankruptcy and the bombing (along with the manhunt) are relevant to many people regardless of their regular community membership in communication networks. These events prompt more communication across community boundaries (there is increased communication between users of different communities). As a consequence of the occurrence of global events, communication patterns become more diverse.

Our work exhibits the following limitations. First, the proposed method depends on the definition of an initial community partition. The initial community partition is defined by aggregating the network activity during a fixed period of time (i.e., controlled through m0). By relying on this strategy, we guarantee that the majority of the users are going to be identified with a community partition and that subsequent interactions in the communication networks can be classified with respect to inter- or intra-community links. However, there is a lot of freedom on the strategy to prepare and update this “normal” community structure. Second, to make the decision on whether a particular data sample includes an event, our reasoning is based on the distance of the observation with respect the moving average of the measure. We implement this criterion by accounting for the number of moving standard deviations (i.e., controlled through δ in Algorithm 1). Clearly, defining how many standard deviations are needed to establish a detection threshold depends on many factors. In the two case studies, the frequency of the formation of the networks is one week for Enron and one hour for the Twitter datasets. Third, in evaluating the performance of the algorithms, the detection intervals are assumed to be proportional to the network formation intervals. This means that even when an interval is reported to contain an event, there is no notion of temporality with respect to the closeness of the occurrence of the event within that interval. This might be balanced by increasing the length of detection intervals. However, given the length of the observation periods, a limited detection resolution will decrease the performance of the proposed method.



Enron email communication network

Enron was one of the largest U.S. businesses in the late 90s when it filed for bankruptcy in 200121. The company omitted negative balances and reported inflated profits by allocating losses into fraudulent special purpose entities. After its investigation, the Federal Regulatory Commission published a corpus of Enron’s corporate emails29,30, consisting of over 125000 emails sent by 184 employees. The data can be represented as a directed weighted network in which a node is an employee and a link is the number of emails between two employees. The network describes interactions between 1999-01-01 and 2002-04-30. Across the email exchange that led to the bankruptcy, seven major events have been identified31,32. The events are described in Table 1.

Table 1 Enron’s event description.

Twitter interaction networks during the Boston Marathon bombing

On April 15th 2013, explosions took place during the Boston Marathon33. One of two suspects was shot dead on April 18th and the other was captured on April 19th34. We use over 456 million English tweets, posted during April, to create a mention and a retweet network. The events that we consider are described in Table 2 and have been referenced in previous studies33,34.

Table 2 Boston Marathon bombing event description.

Network representation

Consider the sequence of n equal-sized intervals \(A=\{{A}_{1},{A}_{2},\ldots ,{A}_{n}\}={\{{A}_{k}\}}_{k=1}^{n}\). Let \( {\mathcal H} =\{1,2,\ldots ,N\}\) be the set of nodes (e.g., the set of Enron employees or Twitter users). Let \({\mathscr{V}}(k)\subseteq {\mathcal H} \) be the subset of nodes that interact during interval \({A}_{k}=[{a}_{k},{a^{\prime} }_{k})\). Let \({\mathscr{W}}(k)=\{{\omega }_{ij}(k):i,j\in {\mathscr{V}}(k)\}\) be a weighted adjacency matrix in which \({\omega }_{ij}(k)\) captures the number of interactions between nodes i and j. Let \({\mathscr{G}}(k)=({\mathscr{V}}(k),{\mathscr{W}}(k))\) represent a weighted directed network that takes account of all interactions within interval Ak. Finally, let \(G={\{{\mathscr{G}}(k)\}}_{k=1}^{n}\) denote the sequence of the temporal networks.

Detection problem

The series G captures the dynamics of the network across time and defines the basis for detection. Let m (\(1\le m < n\)) represents the resolution of detection in terms of the number of intervals \({A}_{k},k\in \{1,2,\ldots ,n\}\). For instance, if \(m=2\), then the detection problem is concerned with identifying whether an event occurs within the detection interval \(({a}_{k-m+1},{a^{\prime} }_{k}]=({a}_{k-1},{a^{\prime} }_{k}]\), \(k=1,2,\ldots n\). Let \(\bar{n}=\lfloor \frac{n}{m}\rfloor \) be the number of times an algorithm (with resolution m) assesses detection. Let \(E\subseteq \{1,2,\ldots ,\bar{n}\}\) represent the intervals at which at least one event occurs (based on ground truth information). Define \(e\in E\) as the index of a detection interval containing an event. Let \(\hat{E}\subseteq \{1,2,\ldots ,\bar{n}\}\) represent the set of intervals at which the occurrence of at least one event is reported by the detection method. Similarly \(\hat{e}\in \hat{E}\) represents the index at which an event is reported. The detection problem is defined as follows: Given a series of networks \(G={\{{\mathscr{G}}(k)\}}_{k=1}^{n}\) and a detection resolution m, identify the set of intervals \(\hat{E}\) that contain at least one event.

Method evaluation

The performance of the algorithm is measured based on \(\hat{E}\) and E, with which we calculate ROC and PRC using the values of the generated time series as a threshold.

The proposed detection method

The proposed method identifies detection signatures based on the communication patterns with respect to a network community and not in the change of the community structure. Thus, rather than trying to detect community structure in each time step, we aggregate network snapshots to build an initial network segment at which we apply community detection and consider the resulting partition as the normal community. For each time period, the network snapshot is defined by the communication occurred during the time period.

The initial network segment of length m0 is defined by

$${{\mathscr{G}}}_{{m}_{0}}=({{\mathscr{V}}}_{{m}_{0}},{{\mathscr{W}}}_{{m}_{0}})=\underset{k^{\prime} =1}{\overset{{m}_{0}}{\bigoplus }}\,{\mathscr{G}}(k^{\prime} )={\mathscr{G}}(1)\oplus \cdots \oplus {\mathscr{G}}({m}_{0})$$

where \({{\mathscr{V}}}_{{m}_{0}}={\cup }_{k^{\prime} =1}^{{m}_{0}}\,{\mathscr{V}}(k^{\prime} )\) and \({{\mathscr{W}}}_{{m}_{0}}={\sum }_{k^{\prime} =1}^{{m}_{0}}\,{\mathscr{W}}(k^{\prime} )\). In this network, we consider only reciprocal communications within the largest connected component. We also remove dangling nodes. From the initial network segment \({{\mathscr{G}}}_{{m}_{0}}\), we identify non-overlapping communities, i.e., the set of nodes can be grouped into subsets such that nodes belonging to the same subset are densely interconnected35.

The proposed algorithm reports events based on the proportion of inter- and intra-community links of the network \({{\mathscr{G}}}_{m}(k)\) with respect to \({m}_{0}\). For the Enron network, the community partition used as a reference corresponds to a period of \({m}_{0}=91\) weeks. In this network, \({{\mathscr{G}}}_{{m}_{0}}\) has 222 nodes and 28672 edges before simplification. After simplification, \({{\mathscr{G}}}_{{m}_{0}}\) has 81 nodes and 199 edges. We identify eight communities. For the Twitter networks, m0 corresponds to seven days of user interactions. For the mention network, \({{\mathscr{G}}}_{{m}_{0}}\) has 20597742 nodes and 60147176 edges before simplification. After simplification, \({{\mathscr{G}}}_{{m}_{0}}\) has 3517744 nodes and 3626560 edges. We identify 665656 communities. For the retweet network, \({{\mathscr{G}}}_{{m}_{0}}\) has 12209899 nodes and 26215122 edges before simplification. After simplification, \({{\mathscr{G}}}_{{m}_{0}}\) has 588279 nodes and 433583 edges. We identify 181007 communities.

To define \(\hat{E}\), let \(C({{\mathscr{G}}}_{{m}_{0}})=\{0,1,\ldots ,c\}\) be a set of unique community identifiers, where \(c+1\) is the number of communities in \({{\mathscr{G}}}_{{m}_{0}}\). The community to which node \(i\in {{\mathscr{V}}}_{m}(k)\cap {{\mathscr{V}}}_{{m}_{0}}\) belongs (based on \({{\mathscr{G}}}_{{m}_{0}}\)) is given by \({c}_{i}:i\to C({{\mathscr{G}}}_{{m}_{0}})\). We compute the community partition of \({{\mathscr{G}}}_{{m}_{0}}\) using the Infomap algorithm36. Following similar ideas as in20, let \({I}_{\curvearrowright }({{\mathscr{G}}}_{m}(k))=\{(i,j):{\omega }_{ij}(k) > 0\wedge ({c}_{i}\cap {c}_{j})=\varnothing \}\) represent the set on inter-community links and \({I}_{\circlearrowright }({{\mathscr{G}}}_{m}(k))=\{(i,j):{\omega }_{ij}(k) > 0\wedge ({c}_{i}\cap {c}_{j})\ne \varnothing \}\) the set of intra-community links. Define the inter- and intra-community link ratios as

$${c}_{\curvearrowright }^{m}(k)=\frac{|{I}_{\curvearrowright }({{\mathscr{G}}}_{m}(k)|}{|{I}_{\curvearrowright }({{\mathscr{G}}}_{m}(k))|+|{I}_{\circlearrowright }({{\mathscr{G}}}_{m}(k))|}$$
$${c}_{\circlearrowright }^{m}(k)=\frac{|{I}_{\circlearrowright }({{\mathscr{G}}}_{m}(k)|}{|{I}_{\curvearrowright }({{\mathscr{G}}}_{m}(k))|+|{I}_{\circlearrowright }({{\mathscr{G}}}_{m}(k))|}$$

Detection focuses on identifying the intervals k, for which \({c}_{\curvearrowright }^{m}(k)-{c}_{\circlearrowright }^{m}(k)\) exceeds a threshold that is a function of the mean and the standard deviation. We use the sample mean (over the entire period of the study) as the mean estimator because observations seem to resemble a normal distribution—since hypothesis testing demonstrates that the normal distribution is a good candidate to model the generation of the empirical observations. Moreover, we use the sample standard deviation as the estimator of the standard deviation. The pseudo-code for the detection algorithm is shown in Algorithm 1. The parameter δ controls how many standard deviations are considered to report an event. The parameter τ is the window length in the moving average model.

Algorithm 1

Event-Detection (G, m0, m, δ, τ).

To measure detection performance, we compare the measure of \({c}_{\curvearrowright }^{m}(k)-{c}_{\circlearrowright }^{m}(k)\) with the respective measure derived from \({{\mathscr{G}}}_{m}(k)\), e.g., the volume of interactions—number of links of the cumulative network segment.

Data Availability

The datasets analyzed during the current study are available at (Enron) and (Twitter). The code for the proposed method is available at and released under the GNU General Public License.


  1. 1.

    Chandola, V., Banerjee, A. & Kumar, V. Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41, 15 (2009).

    Article  Google Scholar 

  2. 2.

    Ding, Q., Katenka, N., Barford, P., Kolaczyk, E. & Crovella, M. Intrusion as (Anti)social Communication: Characterization and Detection. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 886–894 (Beijing, China, 2012).

  3. 3.

    Bolton, R. J. et al. Unsupervised Profiling Methods for Fraud Detection. In Credit Scoring and Credit Control VII, 235–255 (Edinburgh, UK, 2001).

  4. 4.

    Wong, W.-K., Moore, A. W., Cooper, G. F. & Wagner, M. M. Bayesian Network Anomaly Pattern Detection for Disease Outbreaks. In Proceedings of the 20th International Conference on Machine Learning, 808–815 (Washington, DC, USA, 2003).

  5. 5.

    Ranshous, S., Shen, S., Harenberg, D. K. S., Faloutsos, C. & Samatova, N. F. Anomaly detection in dynamic networks: A survey. Wiley Interdisciplinary Reviews: Computational Statistics 7, 223–247 (2015).

    MathSciNet  Article  Google Scholar 

  6. 6.

    Fond, T. L., Neville, J. & Gallagher, B. Anomaly Detection in Networks with Changing Trends. In ACM SIGKDD 2014 ODD Workshop on Outlier Detection & Description under Data Diversity, 3–12 (New York, NY, USA, 2014).

  7. 7.

    Noble, C. C. & Cook, D. J. Graph-based anomaly detection. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 631–636 (Washington, DC, USA, 2003).

  8. 8.

    Pincombe, B. Anomaly Detection in Time Series of Graphs Using ARMA Processes. ASOR Bulletin 24 (2005).

  9. 9.

    Sun, J., Faloutsos, C., Papadimitriou, S. & Yu, P. S. GraphScope: Parameter-Free Mining of Large Time-Evolving Graphs. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 687–696 (San Jose, CA, USA, 2007).

  10. 10.

    Hirose, S., Yamanishi, K., Nakata, T. & Fujimaki, R. Network Anomaly Detection Based on Eigen Equation Compression. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1185–1194 (Paris, France, 2009).

  11. 11.

    Akoglu, L. & Faloutsos, C. Event Detection in Time Series of Mobile Communication Graphs. In 27th Army Science Conference, 77–79 (Orlando, FL, USA, 2010).

  12. 12.

    Aggarwal, C. C., Zhao, Y. & Yu, P. Outlier Detection in Graph Streams. In Proceedings of the 27th IEEE International Conference on Data Engineering, 399–409 (Hannover, Germany, 2011).

  13. 13.

    Rossi, R. A., Gallagher, B., Neville, J. & Henderson, K. Modeling Dynamic Behavior in Large Evolving Graphs. In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, 667–676 (Rome, Italy, 2013).

  14. 14.

    Koutra, D., Vogelstein, J. & Faloutsos, C. DeltaCon: A Principled Massive-Graph Similarity Function. In Proceedings of the 13th SIAM International Conference on Data Mining (SDM), 162–170 (Austin, TX, USA, 2013).

  15. 15.

    Peel, L. & Clauset, A. Detecting Change Points in the Large-Scale Structure of Evolving Networks. In 29th AAAI Conference on Artificial Intelligence (AAAI), 2914–2920 (Austin TX, USA, 2015).

  16. 16.

    Sohn, Y. & Park, J. H. Bayesian Approach to Multilayer Stochastic Blockmodel and Network Changepoint Detection. Network Science 5, 164–186 (2017).

    Article  Google Scholar 

  17. 17.

    Duan, D., Li, Y., Jin, Y. & Lu, Z. Community Mining on Dynamic Weighted Directed Graphs. In Proceedings of the 1st ACM International Workshop on Complex Networks Meet Information and Knowledge Management, 11–18 (Hong Kong, China, 2009).

  18. 18.

    Karrer, B., Levina, E. & Newman, M. E. J. Robustness of community structure in networks. Phys. Rev. E 77, 046119 (2008).

    ADS  Article  Google Scholar 

  19. 19.

    Yang, Y., Li, Z., Chen, Y., Zhang, X. & Wang, S. Improving the Robustness of Complex Networks with Preserving Community Structure. PLoS One 10, 1–14 (2015).

    Google Scholar 

  20. 20.

    Weng, L., Menczer, F. & Ahn, Y.-Y. Virality Prediction and Community Structure in Social Networks. Sci. Rep. 3, 2522 (2013).

    ADS  Article  Google Scholar 

  21. 21.

    Diesner, J., Frantz, T. L. & Carley, K. M. Communication Networks from the Enron Email Corpus “It’s Always About the People. Enron is no Different”. Comput. Math. Organ. Theory 11, 201–228 (2005).

    Article  Google Scholar 

  22. 22.

    Collingsworth, B., Menezes, R. & Martins, P. Assessing organizational stability via network analysis. In IEEE Symposium on Computational Intelligence for Financial Engineering (CIFEr), 43–50 (Nashville, TN, USA, 2009).

  23. 23.

    Centola, D. & Macy, M. Complex Contagions and the Weakness of Long Ties. Am. J. Sociol. 113, 702–734 (2007).

    Article  Google Scholar 

  24. 24.

    Berry, M. W. & Browne, M. The 2001 annotated (by topic) Enron email data set, (Last Accessed: April 11, 2017) (2010).

  25. 25.

    Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett 27, 861–874 (2006).

    Article  Google Scholar 

  26. 26.

    Gordon, M. & Kochen, M. Recall-precision trade-off: A derivation. J. Assoc. Inf. Sci. Technol 40, 145–151 (1989).

    Google Scholar 

  27. 27.

    Saito, T. & Rehmsmeier, M. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLoS One 10, 1–21 (2015).

    Google Scholar 

  28. 28.

    Jackson, D. AP Twitter feed hacked; no attack at White House, (Last Accessed: September 25, 2017) (2013).

  29. 29.

    Priebe, C. E., Conroy, J. M., Marchette, D. J. & Park, Y. Scan Statistics on Enron Graphs. Comput. Math. Organ. Theory 11, 229–247 (2005).

    Article  Google Scholar 

  30. 30.

    Wilson, G. & Banzhaf, W. Discovery of Email Communication Networks from the Enron Corpus with a Genetic Algorithm using Social Network Analysis. In IEEE Congress on Evolutionary Computation, 3256–3263 (Trondheim, Norway, 2009).

  31. 31.

    Marks, R. Enron Timeline, (Last Accessed: April 10, 2017) (2010).

  32. 32.

    Darst, R. K. et al. Detection of timescales in evolving complex systems. Sci. Rep. 6, 39713 (2016).

    ADS  CAS  Article  Google Scholar 

  33. 33.

    Sutton, J. et al. Terse Message Amplification in the Boston Bombing Response. In Proceedings of the 11th International ISCRAM Conference, 612–621 (University Park, PA, USA, 2014).

  34. 34.

    Starbird, K., Maddock, J., Orand, M., Achterman, P. & Mason, R. M. Rumors, False Flags, and Digital Vigilantes: Misinformation on Twitter after the 2013 Boston Marathon Bombing. In iConference 2014 Proceedings, 654–662 (Berlin, Germany, 2014).

  35. 35.

    Newman, M. E. J. Detecting community structure in networks. Eur. Phys. J. B 38, 321–330 (2004).

    ADS  CAS  Article  Google Scholar 

  36. 36.

    Rosvall, M. & Bergstrom, C. T. Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. USA 105, 1118–1123 (2008).

    ADS  CAS  Article  Google Scholar 

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We thank Filippo Menczer for helping us analyze the Twitter data. P.M. was supported by Cisco Research under grant #591000. P.M. thanks L. Jean Camp and Steven Rich for their guidance. J.F. was supported in part by the Center of Excellence and Appropriation in Big Data and Data Analytics (CAOBA) and the Colombian Administrative Department of Science, Technology and Innovation (COLCIENCIAS) under grant number FP44842. Y.-Y.A. is supported by the Defense Advanced Research Projects Agency (DARPA), contract W911NF-17-C-0094. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Cisco, COLCIENCIAS, DARPA or the U.S. Government.

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P.M. and Y.-Y.A. conceived the research. P.M., J.F. and Y.-Y.A. design the analysis. P.M. performed the numerical experiments, analyzed the data and prepared the figures. P.M., J.F. and Y.-Y.A. analyzed the results and wrote the manuscript. All authors reviewed the manuscript.

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Correspondence to Pablo Moriano.

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Moriano, P., Finke, J. & Ahn, YY. Community-Based Event Detection in Temporal Networks. Sci Rep 9, 4358 (2019).

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