2,484 research outputs found
The Advantage of Evidential Attributes in Social Networks
Nowadays, there are many approaches designed for the task of detecting
communities in social networks. Among them, some methods only consider the
topological graph structure, while others take use of both the graph structure
and the node attributes. In real-world networks, there are many uncertain and
noisy attributes in the graph. In this paper, we will present how we detect
communities in graphs with uncertain attributes in the first step. The
numerical, probabilistic as well as evidential attributes are generated
according to the graph structure. In the second step, some noise will be added
to the attributes. We perform experiments on graphs with different types of
attributes and compare the detection results in terms of the Normalized Mutual
Information (NMI) values. The experimental results show that the clustering
with evidential attributes gives better results comparing to those with
probabilistic and numerical attributes. This illustrates the advantages of
evidential attributes.Comment: 20th International Conference on Information Fusion, Jul 2017, Xi'an,
Chin
Link-Prediction Enhanced Consensus Clustering for Complex Networks
Many real networks that are inferred or collected from data are incomplete
due to missing edges. Missing edges can be inherent to the dataset (Facebook
friend links will never be complete) or the result of sampling (one may only
have access to a portion of the data). The consequence is that downstream
analyses that consume the network will often yield less accurate results than
if the edges were complete. Community detection algorithms, in particular,
often suffer when critical intra-community edges are missing. We propose a
novel consensus clustering algorithm to enhance community detection on
incomplete networks. Our framework utilizes existing community detection
algorithms that process networks imputed by our link prediction based
algorithm. The framework then merges their multiple outputs into a final
consensus output. On average our method boosts performance of existing
algorithms by 7% on artificial data and 17% on ego networks collected from
Facebook
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