Person Activity Subgraph Features in LDBC DATAGEN

by Arnau Prat / on 04 Feb 2015

When talking about DATAGEN and other graph generators with social
network characteristics, our attention is typically borrowed by the
friendship subgraph and/or its structure. However, a social graph is
more than a bunch of people being connected by friendship relations, but
has a lot more of other things is worth to look at. With a quick view to
commercial social networks like Facebook, Twitter or Google+, one can
easily identify a lot of other elements such as text images or even
video assets. More importantly, all these elements form other subgraphs
within the social network! For example, the person activity subgraph is
composed by posts and their replies in the different forums/groups in a
social network, and has a tree-like structure connecting people through
their message interactions.

When looking at the LDBC Social Network Benchmark (SNB) and its
interactive workload, one realizes that these other subgraphs, and
especially the person activity subgraph, play a role even more important
than that played by the friendship subgraph. Just two numbers that
illustrate this importance: 11 out of the 14 interactive workload
queries needs traversing parts of the person activity subgraph, and
about 80% of all the generated data by DATAGEN belongs to this subgraph.
As a consequence, a lot of effort has been devoted to make sure that the
person activity subgraph is realistic enough to fulfill the needs of the
benchmark. In the rest of this post, I will discuss some of the features
implemented in DATAGEN that make the person activity subgraph

Reaslistic Message Content

Messages' content in DATAGEN is not random, but contains snippets of
text extracted from Dbpedia talking about the tags the message has.
Furthermore, not all messages are the same size, depending on whether
they are posts or replies to them. For example, the size of a post is
selected uniformly between a minimum and a maximum, but also, there is a
small probability that the content is very large (about 2000
characters). In the case of commets (replies to posts), there is a
probability of 0.66 to be very short (“ok”, “good”, “cool”, “thanks”,
etc.). Moreover, in real forum conversations, it is tipical to see
conversations evolving from one topic to another. For this reason, there
is a probability that the tags of comments replying posts to change
during the flow of the conversation, moving from post’s tags to other
related or randomly selected tags.

Non uniform activity levels

In a real social network, not all the members show the same level of
activity. Some people post messages more sporadically than others, whose
activity is significantly higher. DATAGEN reproduces this phenomena by
correlating the activity level with the amount of friends the person
has. That is, the larger the amount of friends a person has, the larger
the number of posts it creates, and also, the larger the number of
groups it belongs to.

Time correlated post and comment generation

In a real social network, user activity is driven by real world events
such as sport events, elections or natural disasters, just to cite a few
of them. For this reason, we observe spikes of activity around these
events, where the amount of messages created increases significantly
during a short period of time, reaching a maximum and then decreasing.
DATAGEN emulates this behavior by generating a set of real world events
about specific tags. Then, when dates of posts and comments are
generated, these events are taken into account in such a way that posts
and comments are clustered around them. Also not all the events are
equally relevant, thus having spikes larger than others. The shape of
the activity is modeled following the model described in [1].
Furthermore, in order to represent the more normal and uniform person
activity levels, we also generate uniformly distributed messages along
the time line. The following figure shows the user activity volume along
the time line.


As we see, the timeline contains spikes of activity, instead of being
uniform. Note that the generally increasing volume activity is due to
the fact that more people is added to the social network as time

In this post we have reviewed several interesting characteristics of the
person activity generation process in DATAGEN. Stay tuned for future
blog posts about this topic.


[1] Leskovec, J., Backstrom, L., & Kleinberg, J. (2009, June).
Meme-tracking and the dynamics of the news cycle. In Proceedings of the
15th ACM SIGKDD international conference on Knowledge discovery and data
(pp. 497-506). ACM.