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Characterizing Individual Communication Patterns

The increasing availability of electronic communication data, such as that arising from e-mail exchange, presents social and information scientists with new possibilities for characterizing individual behavior and, by extension, identifying latent structure in human populations. Here, we propose a model of individual e-mail communication that is sufficiently rich to capture meaningful variability across individuals, while remaining simple enough to be interpretable. We show that the model, a cascading non-homogeneous Poisson process, can be formulated as a double-chain hidden Markov model, allowing us to use an efficient inference algorithm to estimate the model parameters from observed data. We then apply this model to two e-mail data sets consisting of 404 and 6,164 users, respectively, that were collected from two universities in different countries and years. We find that the resulting best-estimate parameter distributions for both data sets are surprisingly similar, indicating that at least some features of communication dynamics generalize beyond specific contexts. We also find that variability of individual behavior over time is significantly less than variability across the population, suggesting that individuals can be classified into persistent"types". We conclude that communication patterns may prove useful as an additional class of attribute data, complementing demographic and network data, for user classification and outlier detection--a point that we illustrate with an interpretable clustering of users based on their inferred model parameters.


INTRODUCTION
Empirical social science has long sought to classify humans according to their individual attributes, whether observable or self-reported, and to construct models based on these attributes that predict or explain other sociological variables of interest, like group membership [25], political preferences [19], or consumer behavior [14]. Traditionally, this modeling exercise has relied on the measurement of demographic attributes [13], like gender, age, race, education and income, that are typically obtained through observational and survey methods. Although straightforward in principle, these methods are generally expensive and time consuming to implement, especially if one wishes to avoid problems associated with sampling bias and respondent reliability [3]. More importantly, demographic attributes are often poor predictors of many of the outcome variablessuch as behaviors, attitudes, and beliefs-that are of interest to social scientists [11].
The explosive growth of Internet-related communication over the past decade is therefore of great interest from a social science perspective as it offers new sources of data, such as e-mail [9,17], instant messaging [20], telephone [10] and social networking records [16], that can be used to approximate sociological relationships and behavior for very large populations in an efficient and reliable manner. Until now, these data have mostly been used to construct social networks, where individuals can then be characterized in terms of structural attributes like degree, centrality, structural equivalence, and community membership [23,32,27]. Typically these structural features are then related to other kinds of individual characteristics, including demographic attributes, organizational affiliations, and outcome variables like social capital [6,24] or career success [5].
In this paper we introduce an alternative approach to characterizing individuals that, like network analysis, takes activity data as input, but focuses instead on the dynamics of the activity rather than the structure of links with others. In order to illustrate the approach, we study e-mail communication activity-that is, we model the time series of outgo-ing messages recorded by an individual's e-mail server-and we also propose a particular model that captures some characteristic features of human communication behavior. Significantly, this approach to modeling e-mail data may find useful applications in areas like spam detection, where the abusive behavior of interest may exhibit temporal patterns that are characteristically "non-human," or may simply be distinguishable from the previous behavior of a given user. However, we emphasize at the outset that neither e-mail data nor the particular model we specify is essential to the approach followed here. Indeed our approach can be applied to other modes of communication, or even other forms of activity, like web-browsing or clickstream data, that generate time series of an individual's discrete actions. We emphasize that temporal patterns in individual behavior serve as an additional set of features, analogous to demographic or network attributes, that may be leveraged either to cluster users into groups of similar individuals or to identify changes in behavior over time.
Related Work. Clearly, modeling of temporal data has been investigated extensively, especially in the context of clustering time series [21], where standard clustering techniques, such as K-means, agglomerative hierarchical clustering or mixture model clustering have been used to identify similarities among parameter estimates for models across a wide range of disciplines [21]. To the best of our knowledge, however, these techniques have not been used specifically to characterize human correspondence patterns or, more generally, human activity patterns. Thus although we also perform some rudimentary clustering of our model parameters, our work should be viewed as complementary to the clustering literature in that our focus is on the formulation and estimation of the model itself, rather than on the techniques used to cluster the resulting parameters.
In more closely related work, several models have recently been proposed specifically to account for empirical regularities in human activity patterns. One class of models in this genre asserts that human communication behavior can be captured by simple, universally applicable rules, where individuals are assumed to decide when to execute tasks based on a priority queue [1,30]. These models have been shown to reproduce some asymptotic statistical properties of communication patterns; however, they also make predictions that are clearly at odds with important features of the empirical data [29,22]. In particular, priority queuing models fail to account for two important features of human communication patterns: first, that individuals are influenced by daily and weekly cycles of activity; and second, that individuals tend to send multiple messages during "sessions" of relatively high activity.
In response to these shortcomings, an alternative class of models has been proposed that explicitly takes these features into account [22]. One such model, a so-called cascading non-homogeneous Poisson process, has been shown to be consistent with at least one empirical data set [9]. Unfortunately, the complexity of the model also makes the corresponding parameter estimations difficult to interpret; thus it is a poor candidate for the particular purpose we have in mind here, namely the characterization of individuals in terms of a relatively small number of interpretable attribute variables. On a practical note, moreover, the relatively complex structure of the model proposed in Ref. [22] requires expensive optimization techniques (e.g. simulated annealing) to infer model parameters-a slow process that renders the approach impractical for data sets larger than a few hundred individuals. Our contributions. To resolve these issues, we propose a simplified parametric version of the model in Ref. [22] that is sufficiently rich to differentiate between individuals in a meaningful way, while remaining simple enough that we can interpret these differences both in terms of individuals' daily and weekly cycles, and also the extent to which they concentrate their effort into sessions. In addition, we exploit the fact that a cascading non-homogeneous Poisson process can be regarded as a Markov mixture of Poisson processes, which can in turn be recast as a variant of the canonical hidden Markov model [26]. We develop the associated expectation-maximization algorithm for computationally efficient parameter inference from the observed data, thereby rendering it practical for estimating model parameters for a large population of users.
To demonstrate the utility of our approach, we apply this model to two university e-mail data sets, and study the distributions of inferred parameters, finding similar results both across universities and over successive semesters. More importantly, we also show that individual parameter estimates fluctuate less over time than they do across individuals, suggesting that individual attributes are relatively persistent, and thus are reasonable candidates for characterizing individuals. To illustrate this point, we refer to the five time series in Fig. 1. Four of these time series depict the times at which outgoing e-mails were recorded by an email server for four separate individuals [17], while the fifth is synthetically generated from our model. Based on visual inspection alone, it is unclear how these time series should be classified [15]. As we will show, however, the synthetic time series can be easily identified in terms of our model parameters, while the remaining users cluster into one of two interpretable categories of human activity patterns. Having identified these categories, we further demonstrate that 75% of individuals remain in the same category over the course of a two-year period, thereby reinforcing our claim that individuals can be usefully characterized in terms of their communication patterns.
The remainder of this manuscript is organized as follows. In section Sec. 2, we formalize our model and show that it can be phrased as a variant of the well-studied hidden Markov model (HMM) [26]. Leveraging this formulation of the model, we implement a computationally efficient expectation-maximization (EM) inference algorithm [7] to estimate model parameters from the observed data, which we validate with synthetic data. In Sec. 3 we describe the two data sets [9,17] studied, and we present the analysis of the inferred parameters of all users under consideration in Sec. 4. Open questions and future directions are discussed in Sec. 5.

MODEL
The cascading non-homogeneous Poisson process we present is motivated by two key observations: first, individuals send e-mail during "sessions" of relatively high activity that are separated by periods of inactivity during which no emails are sent [22]; and second, the likelihood of commencing an active session is modulated by daily and weekly cycles. For convenience, we define the start and end of a session by the first and last e-mails sent in that session respectively. We define an individual as "active" if they are in an e-mail session, where the time between consecutive e-mails within each session is modeled as a homogeneous Poisson process with intra-session rate ρa. Correspondingly, we define an individual as "passive" if they are between e-mail sessions, where the time between sessions is modeled as a non-homogeneous Poisson process with inter-session rate ρ(t), which explicitly accounts for daily and weekly cycles of activity. Finally, we assume that after each communication an individual transitions to the passive state with transition probability ξ. In this way, our model can be viewed as a mixture of two Poisson process whose transitions are governed by a first-order Markov process with control parameter ξ ( Fig. 2A-B).
Before proceeding, we note two features of the inter-session rate that are essential for capturing human activity. First, the inter-session rate ρ(t) depends on time in a periodic manner; that is, ρ(t) = ρ(t + W ) where W , the period of the process, is defined as one week. Second, we parameterize the form of the inter-session rate ρ(t) as where ρp is the average inter-session rate, p d (t) is the probability of starting a session during a particular hour of the day, and pw(t) is the probability of starting a session during a particular day of week. In contrast with previous work [22], we parameterize both p d (t) and pw(t) by a square pulse distribution on a circle of circumference τ = 24 hours and τ = 7 days respectively, where in each case the form of the square pulse is given by −1 by normalization and x denotes arithmetic modulus τ . Moreover, we refer to τ 0d (and respectively τ0w) as the start of the day (week), τ 1d (τ1w) as the end of the day (week), and ε d ∈ [0, 1] (εw) as the modulation during inactive periods of the day (week). This parameterization of the inter-session rate ρ(t) therefore accounts for the heightened tendency of users to commence an Active Passive Figure 2: A cascading non-homogeneous Poisson process for human activity patterns. A, Human activity patterns are characterized by a first-order Markov process with transition rates ξ and 1 − ξ between an active state (zn = 1) and a passive state (zn = 0) of activity. B, In the active state, e-mails are sent at a relatively high intra-session rate ρa (solid red line) whereas in the passive state, e-mails are sent at a relatively low inter-session rate ρ(t) (solid blue line). On average, the rate during the passive state is ρp (dashed blue line). C, Our model can be viewed as a double-chain Markov model where the time between consecutive events ∆tn is drawn from a Poisson process with rate ρa or rate ρ(t). White (grey) circles denote missing (observed) data.
active session during the waking hours of the day and working days of the week (Fig. 2B). For example, if Sid works a "nine to five" job and he checks his e-mails every other weekend, we might expect that he starts sending e-mails at 09:00 (τ 0d = 9), he stops sending e-mails at 18:00 (τ 1d = 18), he is completely inactive at night (ε d = 0), and he only sends e-mails Monday (τ0w = 0) through Friday (τ1w = 5) as well as every other weekend (εw = 0.5).
Our general objective of characterizing individual communication behavior therefore reduces to estimating the parameters θ = {ρp, ρa, ξ, τ 0d , τ 1d , ε d , τ0w, τ1w, εw} for a sequence of observed events. The main technical difficulty associated with this estimation is that we lack knowledge of session information; that is, we only have access to event times without any record of which events belong to which sessions, prohibiting us from directly estimating the intra-session rate ρa, the inter-session rate ρ(t), or the transition probability ξ. To address this issue, we consider the assignments of emails to sessions as missing data, which we then infer from the event sequences. We accomplish this task in an efficient and scalable manner by noting that the model can be specified as a hidden Markov model (HMM); thus expectationmaximization (EM) can be used to simultaneously infer both the assignments of e-mails to sessions and the parameters θ.
To formalize these ideas, we index the observed events by n = 1, . . . , N and denote the time of the n th observation as tn. Importantly, all of the events in this time series are observed over a finite time interval, which, for ease of notation, we will denote by the interval [t0, tN+1). We then introduce the latent variable zn to denote that an individual is passively sending e-mail (zn = 0) between times tn−1 and tn (i.e. the n th event corresponds to the beginning of a new session) or actively sending e-mail (zn = 1) between times tn−1 and tn (i.e. the n th event is a continuation of the current session). We refer to the sets of all observed and latent variables as t = {t0, t1 . . . , tN+1} and z = {z1, . . . , zN+1}, respectively. Given the state of the individual zn, the time ∆tn = tn − tn−1 between two consecutive events is governed by the corresponding rate; that is, the time of event tn depends on both the state of the individual zn and the time of the previous event tn−1. Whereas a standard HMM has first-order couplings between latent variables only, our model has additional first-order couplings between observations. As a result of its graphical model structure (Fig. 2C), it is referred to as a double-chain HMM (DCHMM) 1 . In contrast with the conventional HMM formulation where each element in the Markov chain is separated by equally spaced intervals in time (e.g. Ref. [28]), the Markov chain used here is indexed by event number and the emissions correspond to event times. As such, the elapsed times between elements in the chain are not identical.
Having formulated the model as a DCHMM, we utilize EM to infer the model parameters θ and missing session assignments z from the observed event times t. The EM algorithm iteratively updates the probability distributions over the unobserved session assignments and the maximum-likelihood parameter estimates in a two-step process that proceeds until it converges to a local optimum of the incomplete-data likelihood. In the E-step, the distribution over session assignments, conditioned on the current parameter estimates, is calculated using the forward-backward algorithm [26]. Note that despite the coupling of successive emissions from the DCHMM, the forward-backward algorithm can still be used for efficient calculation of the E-step [2]. In the M-step, the parameter estimates are updated to their maximumlikelihood values, conditioned on the current distribution over session assignments. After the likelihood converges to a locally optimal solution, the procedure is repeated several times (here, 25) to improve the probability of finding the 1 Models with this structure have also been termed autoregressive HMMs (ARHMMs). This term, however, often implies a specific form for the coupling between observations that differs from the one used here, so we refer to the model as a DCHMM to avoid confusion. globally optimal solution. Compared with the O(N 2 ln N ) run time required by simulated annealing inference for the complex model in Ref. [22], EM inference, which requires O(N ) time, offers significant computational savings. For example, EM inference for this model takes less than a minute on a standard desktop computer for a typical time series of 100 events over a 4-month period, whereas parameter inference for the related model in Ref. [22] requires a few hours for the same calculation. Thus, in simplifying the original cascading non-homogeneous Poisson process, we gain not only interpretability, but also the ability to estimate the model for time series with thousands of events and for data sets comprising a large number of individuals (i.e. at least tens of thousands).
Using simulated data generated from our model, we have verified that our EM procedure provides an asymptotic unbiased estimate of model parameters. Specifically, we generated 100 synthetic time series, each with N events from our model with randomly chosen parameters θo. We then used EM to compute the best-estimate parameters b θ for each time series and computed the scaled residuals between the bestestimate parameters and the actual parameter setting b θ −θo where the residuals are scaled by the maximum residual for each parameter. As shown in (Fig. 3), the scaled residuals asymptotically approach zero for each parameter, suggesting that parameter estimates are asymptotically unbiased (where we have confirmed that these results are insensitive to the choice of θo).

DATA SETS
We consider e-mail communication records from two anonymous universities where each e-mail record comprises a sender identifier, a recipient identifier, the size in bytes of the e-mail and a time stamp. The first data set, collected by Eckmann, Moses and Sergi (EMS) [9], comprises e-mail records for 3,188 e-mail accounts at a European university over an 83-day period. The time stamp for each e-mail record has a precision of one second and only e-mails sent between university members are recorded. These data were collected several years ago, before home Internet access was common [8]; thus it is likely, for example, that activity on weekends is systematically lower than for an equivalent contemporary population.
The second data set, originally studied by Kossinets and Watts (KW) [17], comprises 122,133 e-mail accounts recorded from a US university's e-mail server over a 6-semester (2-year) period. Each e-mail record has a precision of one minute. We note that the KW data was collected a few years later than EMS, after it had become common for students to have access to their university e-mail accounts on weekends. Moreover, in contrast with the EMS data set, the KW data includes e-mails within and outside of the university, and also includes additional metadata for each university member including, for instance, each individual's affiliation with the university. Finally, the KW data includes not only students, but also faculty and staff whose behavior and routines we might expect to be systematically different from that of students. To make our comparisons between the EMS and KW data set as relevant as possible, therefore, we restrict our analysis of the KW data set to undergraduate and graduate students during the four non-summer semesters, denoted as semesters 1, 2, 4, and 5.
In addition to their university e-mail account, individuals at each university presumably communicate through several other modes of communication, such as face-to-face conversation, telephone, text messaging, instant messaging, and external e-mail accounts. As a result, some individuals may favor other modes of communication over their university e-mail account, possibly to the point of not using it at all; indeed, this seems to have been the case. EMS User 1962, for example, only sent 5 e-mails while receiving 2,284 e-mails and KW User 3069337 only sent 1 e-mail while receiving 26,230 e-mails. To restrict our study to users who use their university account as a primary mode of electronic communication, therefore, we limit our analysis to individuals who sent, at minimum, approximately 1 e-mail every 2 days. In addition, we consider only e-mail accounts that are likely not listservs, and we collapse all recipient lists of e-mails sent within 5 sec. (1 min.) into one e-mail to be consistent with previous work [22]. As a result of these steps, the data we consider comprise 404 e-mail accounts from the EMS data set which sent at least 41 e-mails over 83 days and 6,164 student e-mail accounts from the KW data set which sent at least 50 e-mails during each of the four non-summer semesters.

RESULTS
We now turn our attention to assessing the feasibility of characterizing user behavior with the parameterization of a simple mechanistic model. First, we compute the bestestimate parameters of all users under consideration for the EMS data set and each of the four non-summer semesters of the KW data set. Since the EMS data set is from a European university in an era before widespread home Internet access whereas the KW data set is from an American university some years later, one might expect that the parameter estimates for the two data sets would be markedly different. As Fig. 4D-I shows, the EMS parameter estimate distributions do in fact differ from the KW distributions for the model parameters which describe daily and weekly fluctuations, presumably due to the strong influence of work cycles on the EMS users. Yet as Fig. 4 shows, the parameter estimate distributions for the two data sets are otherwise surprisingly similar; in particular, the users in the EMS and KW data sets have similar rates of starting sessions, rates of sending e-mails during active intervals, and probabilities of terminating an active interval (Fig. 4A-C).
In addition to comparing the two universities, we have also considered the similarities and differences between the four non-summer semesters of the KW data set. One might reasonably expect that substantial changes would manifest themselves from semester to semester as students enter and leave the university, change their course schedules, and become increasingly reliant on e-mail communication. Consistent with this intuition, we identify three minor systematic changes over the four semesters: the log-mean intersession rate log ρp increases; the log-mean intra-session rate log ρp increases; and the mean probability ξ of terminating an e-mail session decreases. These nonstationarities are indicative of a population that utilizes more e-mail sessions and sending more e-mails per session; however it is unclear whether this increased e-mail usage is due to endogenous factors (e.g. increasingly selecting e-mail as a primary mode of communication), exogenous factors (e.g. increasingly responding to e-mails from others), or a selection bias from considering only those users that sent at least 50 emails in each of the four non-summer semesters. Aside from these minor changes, we find that the KW parameter estimate distributions are markedly similar across successive semesters.
The overall similarity of these distributions, both across different universities and also across different semesters of the same university, therefore suggests that our model does indeed capture salient features of human communication patterns that are common across different times and contexts. From the perspective of using communication data to characterize individual behavior, in other words, these findings are encouraging; however, they also raise an important question: does the heterogeneity observed in the parameter estimate distributions arise from "typical" individuals who approximately change over time in the same manner, or does it instead indicate the presence of heterogeneous individuals whose behavior is relatively stable over time?
To address this question, we next examine the inter-semester fluctuations of the parameter estimates for users within the KW data set and compare these "within-individual" variations (i.e. over time) with the "between-individual" variations (i.e. across the population) of all individuals for any given semester. Specifically, for each of the nine parameters, we define within-individual variability as the standard deviation of the relevant parameter estimated for each user over the four semesters s = 1, 2, 4, 5. Correspondingly, we define the between-individual variability for each parameter as the standard deviation of the relevant distribution averaged over the four semesters. Within-individual variability therefore represents the characteristic scale by which an individual "moves" over time in parameter space, while between-individual variability sets a natural length scale against which the former distance can be measured. In Fig. 5, we plot the complementary cumulative distribution function of the relative variability-the ratio of the within-and between-individual variability-and find that for almost all parameters, more than 80% of individuals vary less over time than they do across the population for any single semester. This finding is significant for two reasons. First, it demonstrates that human behavior cannot be well represented by a single, universal set of canonical rules, as has been claimed previously [31]; rather, individuals have distinct behaviors that persist over extended periods of time relative to the variations between individuals. And second, it suggests that it may be able to exploit these persistent behaviors to classify individuals into "types" according to their activity patterns.
To further explore this possibility, we perform a very simple clustering of users in a sub-space of our model's parameter space. Specifically, we examine the joint two-dimensional distribution of parameters τ 0d and τ 1d , which capture when users start and end their days, respectively. This crosssection of our parameter space is interesting in that obtaining the same information using standard time series clus-tering techniques would be very difficult. We fit a standard two-component Gaussian mixture model using EM to the joint distribution [4] and find that, users can be placed broadly into two clusters, as shown in Fig. 6A. Moreover, the two clusters have clear interpretations arising from the intuitive nature of the model parameters: the first cluster (blue dots) comprises "day laborers" (like our hypothetical example of Sid from earlier) who send e-mail during working hours from 09:00 to 18:00, whereas the second cluster (red crosses) comprises "e-mailaholics" who send e-mail throughout their waking hours from 09:00 to 01:00. Once noted, the distinction between "day laborers" and "e-mailaholics" seems clear, even obvious. Superposing the location of the five users whose time series were depicted in Fig. 1, however, we suggest that it is not at all clear which of the five time series are more similar to one another or what criteria should be used to separate them. Nevertheless, both questions are immediately answered by Fig. 6A; that is, users B and C are clearly "day laborers"), whereas D and E are clearly "e-mailaholics". User A, meanwhile, does not appear to belong to either cluster-an informative exception, as the data corresponding to user A was in fact the time series that was artificially generated, and therefore does not correspond to any "real" user at all. Although in this instance we have hand-picked model parameters outside of the typical range of human activity patterns, this exercise nonetheless demonstrates that it is be possible to detect outlier behavior that may not be easily detectable by other methods. For instance, one might imagine that spammers, like our synthetic user, might disguise their identity by sending e-mails at a rate that mimics a normal pattern, but may give themselves away by starting their "day" at an abnormal time, as indeed we see in Fig. 6A.
Having identified these two distinct clusters of behavior, we can now restate our earlier question about the temporal stability of user behavior more concretely; that is, do individuals retain the same type of behavior from semester to semester or do they switch their behavior depending on the circumstances every semester? We emphasize that our previous results, illustrated in Fig. 5, do not answer this question on their own, as even if individuals tend to fluctuate less over time than the cross-sectional variability of the population, they may still fluctuate enough to switch clusters. As Fig. 6B indicates, however, the vast majority (77%) of individuals are indeed "stable" in the sense that if they start in a given cluster, they will remain in that cluster the next semester-in fact, nearly as many (74%) remain in the same cluster for the entire two year period. In other words, most individuals appear to retain their routines over extended periods of time, in spite of their changing circumstances and course schedules, implying that our method of characterizing them may be broadly applicable. Nevertheless, we note that a sizable minority-roughly 25%-do change clusters over the duration of the KW data. This minority appears to be randomly scattered throughout parameter space, raising interesting and unresolved questions about classifying individual behavioral patterns.

DISCUSSION
In this manuscript, we have introduced a simplified version of the cascading non-homogeneous Poisson process [22] that is amenable to computationally efficient inference techniques suitable for the large scale characterization of human communication patterns. We have applied this model to two e-mail data sets collected from different universities during different years, and we have presented four main findings. First, we find that the distributions of parameter estimates for our model are generally similar across data setsremarkably so given the difference in circumstances under which the data was collected-and stable in time. Second, we find that the parameter estimates for over 80% of individuals in the KW data vary less over the course of four semesters than individuals vary among each other during any one semester. Third, we show that the population can be sensibly partitioned into two distinct clusters that correspond to easily interpretable differences in individual behavior. And finally, we find that our earlier characterization of individuals as "stable" over time also applies to these clusters: roughly 75% of users remain in the same cluster over a two-year interval.
As suggested by our analysis of user clusters, inferred model parameters can be used as features for the tasks of clustering, classification, outlier detection, or change-point analysis [28] in large-scale online systems. Although initial parameter inference takes on the order of minutes for most users, the developed algorithms can easily be parallelized on a per-user basis, and on-line HMM inference algorithms [18] can be leveraged for inexpensive updates to model parameters as new data are recorded. Future directions for work include evaluation of the model parameters as features for the task of classifying known malicious users.
Although we have focused on a cascading non-homogeneous Poisson process in this manuscript, we conclude by reiterating that our principal motivation is to leverage the rapidly increasing volume of communication data-and even more generally, activity data-to characterize individuals. From this perspective, the details of the particular model are of secondary importance, and we anticipate that other models may be more appropriate in different contexts or for different sorts of communication data. The emphasis here is that, by using a simple mechanistic model that captures the salient features of human activity, it is possible to meaningfully characterize human activity and to use this characterization as another attribute of sociological study.
We do not claim, however, that individual attributes extracted from communication or activity data are more or less informative than demographic or network-based attributes. Depending on the question of interest and the available data, one of these approaches may be more suitable than others, and further work is needed to address this issue, possibly comparing the predictive or explanatory power of the various methods directly. Alternatively, one might also consider hybrid approaches that make use of more than one kind of data, analogous to "social targeting" methods [12] which leverage network data to substitute for missing attribute data, based on the so-called "homophily principle" [25] that friends are more similar than strangers. In the same vein, one might leverage observable communication patterns to predict unobservable categorical data, like affiliation or status. For example, do faculty communicate differently than students, or do highly central nodes also distinguish themselves in their communication patterns? Regardless of the specific applications, we anticipate that with the increasing availability of communication data, the dynamics of communication will serve as a useful attribute in understanding the behavior of individuals.