66,336 research outputs found
Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks
Networks are a general language for representing relational information among
objects. An effective way to model, reason about, and summarize networks, is to
discover sets of nodes with common connectivity patterns. Such sets are
commonly referred to as network communities. Research on network community
detection has predominantly focused on identifying communities of densely
connected nodes in undirected networks.
In this paper we develop a novel overlapping community detection method that
scales to networks of millions of nodes and edges and advances research along
two dimensions: the connectivity structure of communities, and the use of edge
directedness for community detection. First, we extend traditional definitions
of network communities by building on the observation that nodes can be densely
interlinked in two different ways: In cohesive communities nodes link to each
other, while in 2-mode communities nodes link in a bipartite fashion, where
links predominate between the two partitions rather than inside them. Our
method successfully detects both 2-mode as well as cohesive communities, that
may also overlap or be hierarchically nested. Second, while most existing
community detection methods treat directed edges as though they were
undirected, our method accounts for edge directions and is able to identify
novel and meaningful community structures in both directed and undirected
networks, using data from social, biological, and ecological domains.Comment: Published in the proceedings of WSDM '1
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
There is often latent network structure in spatial and temporal data and the
tools of network analysis can yield fascinating insights into such data. In
this paper, we develop a nonparametric method for network reconstruction from
spatiotemporal data sets using multivariate Hawkes processes. In contrast to
prior work on network reconstruction with point-process models, which has often
focused on exclusively temporal information, our approach uses both temporal
and spatial information and does not assume a specific parametric form of
network dynamics. This leads to an effective way of recovering an underlying
network. We illustrate our approach using both synthetic networks and networks
constructed from real-world data sets (a location-based social media network, a
narrative of crime events, and violent gang crimes). Our results demonstrate
that, in comparison to using only temporal data, our spatiotemporal approach
yields improved network reconstruction, providing a basis for meaningful
subsequent analysis --- such as community structure and motif analysis --- of
the reconstructed networks
Debiasing Community Detection: The Importance of Lowly-Connected Nodes
Community detection is an important task in social network analysis, allowing
us to identify and understand the communities within the social structures.
However, many community detection approaches either fail to assign low degree
(or lowly-connected) users to communities, or assign them to trivially small
communities that prevent them from being included in analysis. In this work, we
investigate how excluding these users can bias analysis results. We then
introduce an approach that is more inclusive for lowly-connected users by
incorporating them into larger groups. Experiments show that our approach
outperforms the existing state-of-the-art in terms of F1 and Jaccard similarity
scores while reducing the bias towards low-degree users
The Legitimate Route to the Scientific Truth - The Gondor Principle
We leave in a beautiful and uniform world, a world where everything probable is possible. Since the epic theory of relativity many scientists have embarked in a pursuit of astonishing theoretical fantasies, abandoning the prudent and logical path to scientific inquiry. The theory is a complex theoretical framework that facilitates the understanding of the universal laws of physics. It is based on the space-time continuum fabric abstract concept, and it is well suited for interpreting cosmic events. However, it is not well suited for handling of small, local topics as global warming, local energy issues, and overall common humanity matters. We now forward may fancy theories and spend unimaginable effort to validate them, even when we are perhaps headed in a wrong direction. For example, in our times matters of climate changes are debated by politicians based on economical considerations that are as illogical as they come. The venerable paths of scientific method developed during centuries by prominent scientists and philosophers has been willingly ignored and abandoned for various and prejudiced purpose. Contact email: [email protected]
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