Conrad Lee

Simmelian backbones: amplifying hidden homophily in Facebook networks

Empirical social networks are often aggregate proxies for several heterogeneous relations. In online social networks, for instance, interactions related to friendship, kinship, business, interests, and other relationships may all be represented as catchall “friendships.” Because several relations are mingled into one, the resulting networks exhibit relatively high and uniform density. As a consequence, the variation in positional differences and local cohesion may be too small for reliable analysis. We introduce a method to identify the essential relationships in networks representing social interactions. Our method is based on a novel concept of triadic cohesion that is motivated by Simmels concept of membership in social groups. We demonstrate that our Simmelian backbones are capable of extracting structure from Facebook interaction networks that makes them easy to visualize and analyze. Since all computations are local, the method can be restricted to partial networks such as ego networks, and scales to big data.

Link prediction with social vector clocks

State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vector clocks, an adaptation of the vector-clock concept introduced in distributed computing to social interaction networks. In fact, our experiments suggest that by taking into account the order and spacing of interactions, social vector clocks exploit different aspects of link formation so that their combination with previous approaches yields the most accurate predictor to date.

Community detection: effective evaluation on large social networks

While many recently proposed methods aim to detect network communities in large datasets, such as those generated by social media and telecommunications services, most evaluation (i.e. benchmarking) of this research is based on small, hand-curated datasets. We argue that these two types of networks differ so significantly that, by evaluating algorithms solely on the smaller networks, we know little about how well they perform on the larger datasets. Recent work addresses this problem by introducing social network datasets annotated with meta-data that is believed to approximately indicate a ‘ground truth’ set of network communities. While such efforts are a step in the right direction, we find this meta-data problematic for two reasons. First, in practice, the groups contained in such meta-data may only be a subset of a network’s communities. Second, while it is often reasonable to assume that meta-data is related to network communities in some way, we must be cautious about assuming that these groups correspond closely to network communities. Here, we consider these difficulties and propose an evaluation scheme based on a classification task that is tailored to deal with them.

Seeding for pervasively overlapping communities.

In some social and biological networks, the majority of nodes belong to multiple communities. It has recently been shown that a number of the algorithms specifically designed to detect overlapping communities do not perform well in such highly overlapping settings. Here, we consider one class of these algorithms, those which optimize a local fitness measure, typically by using a greedy heuristic to expand a seed into a community. We perform synthetic benchmarks which indicate that an appropriate seeding strategy becomes more important as the extent of community overlap increases. We find that distinct cliques provide the best seeds. We find further support for this seeding strategy with benchmarks on a Facebook network and the yeast interactome.

The geographic flow of music

The social media website last.fm provides a detailed snapshot of what its users in hundreds of cities listen to each week. After suitably normalizing this data, we use it to test one hypothesis related to the geographic flow of music: that some cities are consistently early adopters of new music (and early to snub stale music). To test this hypothesis, we adapt a method previously used to detect the leadership networks present in flocks of birds. We find empirical support for the claim that a similar leadership network exists among cities.

An assessment of community finding algorithms for community-based message routing in DTNs

Previous work has demonstrated that community-finding algorithms can provide useful information for routing algorithms in delay tolerant networks. In this work we investigate which community finding algorithm most effectively informs this routing task. While early community finding algorithms partitioned networks into flat disjoint communities, more recent methods return community structures that can be overlapping and hierarchical. Given this diversity, it seems reasonable to expect that some methods will be better suited to message routing than others. In this paper, we evaluate a number of community finding strategies and find that Link Clustering, which returns overlapping hierarchical clusters, is very effective. We also find that InfoMap performs well --- this is somewhat surprising given that InfoMap returns a flat partition of the network, however this may be because the optimization that drives InfoMap is based on flow.