4,576 research outputs found
Community Detection Using A Neighborhood Strength Driven Label Propagation Algorithm
Studies of community structure and evolution in large social networks require
a fast and accurate algorithm for community detection. As the size of analyzed
communities grows, complexity of the community detection algorithm needs to be
kept close to linear. The Label Propagation Algorithm (LPA) has the benefits of
nearly-linear running time and easy implementation, thus it forms a good basis
for efficient community detection methods. In this paper, we propose new update
rule and label propagation criterion in LPA to improve both its computational
efficiency and the quality of communities that it detects. The speed is
optimized by avoiding unnecessary updates performed by the original algorithm.
This change reduces significantly (by order of magnitude for large networks)
the number of iterations that the algorithm executes. We also evaluate our
generalization of the LPA update rule that takes into account, with varying
strength, connections to the neighborhood of a node considering a new label.
Experiments on computer generated networks and a wide range of social networks
show that our new rule improves the quality of the detected communities
compared to those found by the original LPA. The benefit of considering
positive neighborhood strength is pronounced especially on real-world networks
containing sufficiently large fraction of nodes with high clustering
coefficient.Comment: IEEE NSW 201
Community detection by label propagation with compression of flow
The label propagation algorithm (LPA) has been proved to be a fast and
effective method for detecting communities in large complex networks. However,
its performance is subject to the non-stable and trivial solutions of the
problem. In this paper, we propose a modified label propagation algorithm LPAf
to efficiently detect community structures in networks. Instead of the majority
voting rule of the basic LPA, LPAf updates the label of a node by considering
the compression of a description of random walks on a network. A multi-step
greedy agglomerative strategy is employed to enable LPAf to escape the local
optimum. Furthermore, an incomplete update condition is also adopted to speed
up the convergence. Experimental results on both synthetic and real-world
networks confirm the effectiveness of our algorithm
Community Detection in Dynamic Networks via Adaptive Label Propagation
An adaptive label propagation algorithm (ALPA) is proposed to detect and
monitor communities in dynamic networks. Unlike the traditional methods by
re-computing the whole community decomposition after each modification of the
network, ALPA takes into account the information of historical communities and
updates its solution according to the network modifications via a local label
propagation process, which generally affects only a small portion of the
network. This makes it respond to network changes at low computational cost.
The effectiveness of ALPA has been tested on both synthetic and real-world
networks, which shows that it can successfully identify and track dynamic
communities. Moreover, ALPA could detect communities with high quality and
accuracy compared to other methods. Therefore, being low-complexity and
parameter-free, ALPA is a scalable and promising solution for some real-world
applications of community detection in dynamic networks.Comment: 16 pages, 11 figure
Detecting Strong Ties Using Network Motifs
Detecting strong ties among users in social and information networks is a
fundamental operation that can improve performance on a multitude of
personalization and ranking tasks. Strong-tie edges are often readily obtained
from the social network as users often participate in multiple overlapping
networks via features such as following and messaging. These networks may vary
greatly in size, density and the information they carry. This setting leads to
a natural strong tie detection task: given a small set of labeled strong tie
edges, how well can one detect unlabeled strong ties in the remainder of the
network?
This task becomes particularly daunting for the Twitter network due to scant
availability of pairwise relationship attribute data, and sparsity of strong
tie networks such as phone contacts. Given these challenges, a natural approach
is to instead use structural network features for the task, produced by {\em
combining} the strong and "weak" edges. In this work, we demonstrate via
experiments on Twitter data that using only such structural network features is
sufficient for detecting strong ties with high precision. These structural
network features are obtained from the presence and frequency of small network
motifs on combined strong and weak ties. We observe that using motifs larger
than triads alleviate sparsity problems that arise for smaller motifs, both due
to increased combinatorial possibilities as well as benefiting strongly from
searching beyond the ego network. Empirically, we observe that not all motifs
are equally useful, and need to be carefully constructed from the combined
edges in order to be effective for strong tie detection. Finally, we reinforce
our experimental findings with providing theoretical justification that
suggests why incorporating these larger sized motifs as features could lead to
increased performance in planted graph models.Comment: To appear in Proceedings of WWW 2017 (Web-science track
Inner and Inter Label Propagation: Salient Object Detection in the Wild
In this paper, we propose a novel label propagation based method for saliency
detection. A key observation is that saliency in an image can be estimated by
propagating the labels extracted from the most certain background and object
regions. For most natural images, some boundary superpixels serve as the
background labels and the saliency of other superpixels are determined by
ranking their similarities to the boundary labels based on an inner propagation
scheme. For images of complex scenes, we further deploy a 3-cue-center-biased
objectness measure to pick out and propagate foreground labels. A
co-transduction algorithm is devised to fuse both boundary and objectness
labels based on an inter propagation scheme. The compactness criterion decides
whether the incorporation of objectness labels is necessary, thus greatly
enhancing computational efficiency. Results on five benchmark datasets with
pixel-wise accurate annotations show that the proposed method achieves superior
performance compared with the newest state-of-the-arts in terms of different
evaluation metrics.Comment: The full version of the TIP 2015 publicatio
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