17 research outputs found
Multislice Modularity Optimization in Community Detection and Image Segmentation
Because networks can be used to represent many complex systems, they have
attracted considerable attention in physics, computer science, sociology, and
many other disciplines. One of the most important areas of network science is
the algorithmic detection of cohesive groups (i.e., "communities") of nodes. In
this paper, we algorithmically detect communities in social networks and image
data by optimizing multislice modularity. A key advantage of modularity
optimization is that it does not require prior knowledge of the number or sizes
of communities, and it is capable of finding network partitions that are
composed of communities of different sizes. By optimizing multislice modularity
and subsequently calculating diagnostics on the resulting network partitions,
it is thereby possible to obtain information about network structure across
multiple system scales. We illustrate this method on data from both social
networks and images, and we find that optimization of multislice modularity
performs well on these two tasks without the need for extensive
problem-specific adaptation. However, improving the computational speed of this
method remains a challenging open problem.Comment: 3 pages, 2 figures, to appear in IEEE International Conference on
Data Mining PhD forum conference proceeding
Geosocial Graph-Based Community Detection
We apply spectral clustering and multislice modularity optimization to a Los
Angeles Police Department field interview card data set. To detect communities
(i.e., cohesive groups of vertices), we use both geographic and social
information about stops involving street gang members in the LAPD district of
Hollenbeck. We then compare the algorithmically detected communities with known
gang identifications and argue that discrepancies are due to sparsity of social
connections in the data as well as complex underlying sociological factors that
blur distinctions between communities.Comment: 5 pages, 4 figures Workshop paper for the IEEE International
Conference on Data Mining 2012: Workshop on Social Media Analysis and Minin
Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa
Neural networks are capable of learning rich, nonlinear feature
representations shown to be beneficial in many predictive tasks. In this work,
we use these models to explore the use of geographical features in predicting
colorectal cancer survival curves for patients in the state of Iowa, spanning
the years 1989 to 2012. Specifically, we compare model performance using a
newly defined metric -- area between the curves (ABC) -- to assess (a) whether
survival curves can be reasonably predicted for colorectal cancer patients in
the state of Iowa, (b) whether geographical features improve predictive
performance, and (c) whether a simple binary representation or richer, spectral
clustering-based representation perform better. Our findings suggest that
survival curves can be reasonably estimated on average, with predictive
performance deviating at the five-year survival mark. We also find that
geographical features improve predictive performance, and that the best
performance is obtained using richer, spectral analysis-elicited features.Comment: 8 page
Community Detection from Location-Tagged Networks
Many real world systems or web services can be represented as a network such
as social networks and transportation networks. In the past decade, many
algorithms have been developed to detect the communities in a network using
connections between nodes. However in many real world networks, the locations
of nodes have great influence on the community structure. For example, in a
social network, more connections are established between geographically
proximate users. The impact of locations on community has not been fully
investigated by the research literature. In this paper, we propose a community
detection method which takes locations of nodes into consideration. The goal is
to detect communities with both geographic proximity and network closeness. We
analyze the distribution of the distances between connected and unconnected
nodes to measure the influence of location on the network structure on two real
location-tagged social networks. We propose a method to determine if a
location-based community detection method is suitable for a given network. We
propose a new community detection algorithm that pushes the location
information into the community detection. We test our proposed method on both
synthetic data and real world network datasets. The results show that the
communities detected by our method distribute in a smaller area compared with
the traditional methods and have the similar or higher tightness on network
connections