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the internet has revolutionized science and society in many ways .
studies of human communication networks nowadays deal with data sets of thousands people or more . gone are the days when one had to rely solely on interview surveys and observational studies . these new and larger data sets open up new possibilities
 one does not only get higher precision in the measurement of network structure , one can also rid the structural quantities of finite size effects that would hinder the prediction of phenomena such as large - scale information spreading .
the accessibility of large data sets has also drawn the interest of statistical physicists , habitually working in the large - scale limit , to this traditionally sociological field .
the sociologists themselves , naturally , contribute to the development , and make the study of communication networks a thriving interdisciplinary arena .
internet has , of course , changed other aspects of society than science . in the perennial human quest to find a partner , romantic or
otherwise , the internet provides a new _ modus operandi_. in this paper we study a swedish internet community , pussokram.com , intended for romantic communication among adolescents .
the methods we propose do not only work for dating communities , but for internet communities in general .
our definition of an internet community is a set of html - pages facilitating some type of directed messages ( i.e.  not all messages are broadcasted to the whole community ) , and where each member is associated with a home page .
this definition includes social networking sites @xcite but excludes , for example , news groups @xcite .
the data we use , the same as in ref .
@xcite , are anonymized lists of contacts 
triples @xmath0 where @xmath1 is the id - number of the sender , @xmath2 is the id - number of the receiver , and @xmath3 is the time ( in seconds ) since an arbitrary start time .
the ids are integer numbers with no relation to the users of the real community .
we do not have access to the messages themselves , nor the individual presentations on the user homepages ( or any other text or images either ) .
researched data sets of electronic communication have often been constructed from email exchange  , and to a lesser extent from communication within internet communities  @xcite .
the advantage with internet communities is that they are a closed system  all communication can be recorded . the network of emails is a much larger and more important phenomenon . but studies of email networks are plagued by statistical biases due to the openness of the system . for email communication one typically samples a set of individuals and either restricts the data to messages sent within the group ( which leaves the message set incomplete )  @xcite or one includes contacts to outer vertices , which does not include contact between outer vertices .
for this reason the study of internet communities may not only be of interest _ per se _ , but is also illuminating towards the general structure of electronic communication .    the goal of this paper is to say something about what makes a user successful in the community . given our very restricted data 
needless to say , the most relevant information is in the text and imagery we do not have  is there anything that can be said ?
we propose simple measures for attractiveness and activity and observe that they are positively correlated in the contact sequences .
more than that , these two quantities are more strongly correlated for the real - world data than for the null model we propose .
it thus pays off , directly or ( more likely ) indirectly , to be an active community member .
.[tab : sizes ] the sizes of our three data sets : the number of vertices @xmath4 , edges @xmath5 and contacts @xmath6 . [ cols="<,^,^,^",options="header " , ]     in the previous section we defined three vertex - specific measures for internet community contact sequences .
we measured the probability distribution and the correlations between the measures .
the design of the measures does not exclude that correlations may be induced by the growth of the community . to frame the correlations associated with the psychology of the community members
, we need something to compare the values with , i.e.  a null model .
in fact , we propose two null models , one more and one less restricted . we follow the general approach of ref .
@xcite and sample randomizations of the real - world data set rather than constructing parametric null models . in both these models
we will keep the sizes @xmath4 , @xmath5 and @xmath6 the same as in the original contact sequences . not only that
, we assume the growth of @xmath4 is unrelated to the psychology of the community members , we also keep the time evolution of @xmath4 the same as in the real data . furthermore ,
as the community grows with time the communication rate in the data should also be growing . with a parametric null model
this would be hard to implement , but we just keep the set of times from the real data and the communication rate exactly the same as in the real - world contact sequence .    in the first , less constrained , model we apply the restrictions above .
a randomized contact sequence is constructed as follows :    1 .
[ enu:1c ] for each vertex @xmath1 add one contact to or from ( with equal probability ) at time @xmath7 .
the other vertex of the contact is chosen with uniform randomness among the vertices present in the data at this time .
[ enu : rest ] draw @xmath8 times randomly from the set of non - first contacts .
( i.e.  contacts @xmath0 , such that @xmath9 and @xmath10 . )
add contacts between vertices present in the community at these times .
step [ enu:1c ] ensures that the number of active members is the same in the randomized sequence as in the real - world data .
step [ enu : rest ] makes the set of times almost equal to the empirical set of times .
note that , contacts between two vertices @xmath1 and @xmath2 at @xmath11 will give rise to two contacts in the randomized sequence ; so the set of non - first contacts used in step [ enu : rest ] will be slightly ( @xmath12 ) larger than @xmath8 .    as mentioned in the previous section
a broad activity distribution has been observed in many data sets similar to ours .
one may argue that activity is a rather independent trait , little connected to the contact dynamics . whether or not this is true
, we construct a null model to test how the reality differs from such a scenario . to generate a random sequence realization for this model we loop over all contacts @xmath0 and replace @xmath2 ( the vertex the message is sent to ) by a random vertex @xmath13 present in the community at time @xmath3 .
clearly this procedure conserves @xmath6 .
it may result in a few isolated vertices ( vertices of zero out - degree who looses their incoming links ) , and a slightly altered time evolution of @xmath4 ( vertices whose first contact is incoming may appear later in the randomized community than in the real - world data ) . for our data these effects are small , and we assume them to be negligible .
values of the linear ( pearson s ) correlation coefficient between attractiveness , activity and participance rates for the real and null model networks can be found in table  [ tab : corr ] .
first we note that the attractiveness and activity are more strongly correlated in the real community than in both models , and that this holds for all three networks .
this means that the correlation between attractiveness and activity is not an artifact of the growth of the community ( by comparison with model 1 ) , neither is it a result of a skewed distribution of a ( hypothetical ) intrinsic activity ( by comparison with model 2 ) . unlike the attractiveness vs.  activity correlation
, the correlation between activity and participance rate seems to a large extent to be an effect of the growth of the network .
we see that model 2 induces a very high correlation between activity and participance rate .
model 2 randomizes the recipients but keeps the rest of the communication the same as in the original data .
the fact that the users engage in dialogs is an explanation for the lower correlation in the real world data  two users sending contacts to only each other , but frequently is active but have low participance ratio .
the correlation between the participance rate and attractiveness of the real data is even more different from the null model than the attractiveness - activity correlation .
this means that the vertices of highest degree in the social network generated by the communication are also the one that most frequently get incoming new contacts .
another interesting observation is that the messages data have higher attractiveness correlations than the guest book and all contacts data .
since the messages are not publicly visible one can assume that the real romantic communication takes place here rather than in the guest book writing .
this strengthen the conclusion that active users are attractive in internet dating .
we note that ( spearman type ) rank correlations are typically a little stronger than the linear correlations , but since they add little new information to the discussion we do not include them in our tables .
in this paper we have introduced three measures to characterize users in online communities : attractiveness , activity and participance rate ( measuring how fast a user get to know others in the community ) .
while these three measures are sensible in general internet communities , we evaluate them for an internet dating community  @xcite . how and why people end up as partners is well studied in an off - line setting  @xcite ; attractiveness in terms of body shape  @xcite , facial characteristics  @xcite and body odor  @xcite are all well - known . with internet communities playing an increasingly important role on the dating scene
@xcite one need to complement the studies of in - real - life attractiveness with investigations of online romantic preferences . in this paper
we find that the attractiveness ( the number of incoming new contacts per time ) is positively correlated with activity ( the number of contacts taken per time ) , and that this correlation is significantly stronger than for our two null models .
we find this heightened correlation in the real - world data even stronger in the dataset of e - mail - like messages than in the other data sets ( including messages visible to the whole community ) .
since the activity , as we measure it , is rather invisible to other members , this correlation has to be a secondary effect .
( writing in guest books increases ones visibility and is a , presumably small , direct effect . )
it is natural to assume that the users that send many messages , and thus invest much time into their community , also spend comparatively more time embellishing their homepages .
the conclusion is thus that it pays off to spend effort on one s personal presentation .
another factor is that highly active users are most likely logged in more often than low - activity users .
since currently logged in users are displayed , the increased visibility of frequently logged in members may boost the attractiveness of active users .
we also find highly skewed , power - law - like distributions of attractiveness , activity and participance rate .
the mechanisms behind electronic communication and offline behavior are presumably very different . nevertheless , similar quantities in off - line relationships , such as the number of partners per time , are also known have this feature  @xcite .
research on the structure and dynamics of internet communities is still a young field .
their statistical advantages ( being closed systems ) compared to e - mail exchange make us anticipate much future work with both data analysis and modeling approaches .
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association for computing machinery .
datasets of online communication often take the form of contact sequences  ordered lists contacts ( where a contact is defined as a triple of a sender , a recipient and a time ) . 
 we propose measures of attractiveness and activity for such data sets and analyze these quantities for anonymized contact sequences from an internet dating community . for this data 
 set the attractiveness and activity measures show broad power - law like distributions . 
 our attractiveness and activity measures are more strongly correlated in the real - world data than in our reference model . 
 effects that indirectly can make active users more attractive are discussed .    
 internet community ; network dynamics ; online dating