Low-rank Estimation Based Evolutionary Clustering for Community Detection in Temporal Networks

Al-sharoa, Esraa; Al-khassaweneh, Mahmood; Aviyente, Selin

doi:10.1109/icassp.2019.8682987

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…A data point, x, has the values and max , which are: the weight value of the point right before it was marked as noise, and the lowest propagated child death level from its last labeled cluster, respectively. The value of the outlier score for a point x can be obtained using Equation (2).…”

Section: The Hdbscan* Algorithmmentioning

confidence: 99%

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An Implementation of the HDBSCAN* Clustering Algorithm

Stewart

Al-khassaweneh

2022

Applied Sciences

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An implementation of the HDBSCAN* clustering algorithm, Tribuo Hdbscan, is presented in this work. The implementation is developed as a new feature of the Java machine learning library Tribuo. This implementation leverages concurrency and achieves better performance than the reference Java implementation. Tribuo Hdbscan provides prediction functionality, which is a novel technique to make fast predictions for unseen data points using an HDBSCAN* clustering model. Tribuo Hdbscan cluster results and performance measurements are also compared with the state-of-the-art HDBSCAN* implementation, the Python module hdbscan.

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Section: The Hdbscan* Algorithmmentioning

confidence: 99%

“…It is an unsupervised machine learning task that can discover patterns or identify related groups from a dataset. There are many different algorithms proposed for cluster analysis [1,2]. The K-Means algorithm is commonly used for clustering since it is fast and easy to understand.…”

Section: Introductionmentioning

confidence: 99%

An Implementation of the HDBSCAN* Clustering Algorithm

Stewart

Al-khassaweneh

2022

Applied Sciences

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“…In order to evaluate the performance of the proposed RCDG algorithm in detecting the community structure in binary networks, two network benchmarks are adopted. First, the classical Girvan-Newman benchmark 7 introduced in [13], where the network is divided into k equal sized clusters and each node has a fixed number of internal and external edges. As the number of internal edges, z i , increases the density of the network increases and as the number of the external edges, z e , decreases the clusters become more distinct.…”

Section: Simulated Binary Networkmentioning

confidence: 99%

“…In particular, objects in the system and the interactions between them can be modeled as the nodes and edges of the network, respectively. One of the most popular approaches used in investigating and analyzing networks is community detection [6], [2], [7]. Community detection methods reflect the organization of the nodes into clusters or communities where the nodes in each community share common properties.…”

Section: Introductionmentioning

confidence: 99%

Robust Community Detection in Graphs

2021

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Community detection in network-type data provides a powerful tool in analyzing and understanding real-world systems. In fact, community detection approaches aim to reduce the network's dimensionality and partition it into a set of disjoint clusters or communities. However, real networks are usually corrupted with noise or outliers which affect the detected community structure quality. In this paper, a new robust community detection algorithm that is capable of recovering a clean or a smoothed version of the corrupted graph and detecting the correct community structure is introduced. The proposed approach combines robust principal component analysis (RPCA) and symmetric nonnegative matrix factorization (SymNMF) in a single optimization problem. The proposed problem is solved under the framework of alternating direction methods of multipliers (ADMM). In particular, the corrupted adjacency matrix is decomposed into a low-rank and sparse components using RPCA and the community structure is detected by applying SymNMF to the extracted low-rank component. Extensive experiments that have been conducted on real and simulated binary and weighted networks show that the proposed approach significantly outperforms existing algorithms in detecting the correct community structure even in grossly corrupted networks.

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“…Amelio and Pizzuti [25] use a time-space enhanced clustering for community discovery. Al-Sharoa et al [26] proposed a low-rank approximation based evolutionary clustering approach. The proposed approach provided robustness to outliers and results in smoothly evolving cluster assignments through joint low-rank approximation and subspace learning.…”

Section: Related Workmentioning

confidence: 99%

OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering

Lin

Zhao

2019

Energies

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Customers’ electricity consumption behavior can be studied from daily load data. Studying the daily load data for user behavior pattern analysis is an emerging research area in smart grid. Traditionally, the daily load data can be clustered into different clusters, to reveal the different categories of consumption. However, as user’s electricity consumption behavior changes over time, classical clustering algorithms are not suitable for tracing the changes, as they rebuild the clusters when clustering at any timestamp but never consider the relationship with the clusters in the previous state. To understand the changes of consumption behavior, we proposed an optimized evolutionary clustering (OPEC) algorithm, which optimized the existing evolutionary clustering algorithm by joining the Proper Restart (PR) Framework. OPEC relied on the basic fact that user’s energy consumption behavior would not abruptly change significantly, so the clusters would change progressively and remain similar in adjacent periods, except for an emergency. The newly added PR framework can deal with a situation where data changes dramatically in a short period of time, and where the former frameworks of evolutionary clustering do not work well. We evaluated the OPEC based on daily load data from Shanghai, China and the power load diagram data from UCI machine learning repository. We also carefully discussed the adjustment of the parameter in the optimized algorithm and gave an optimal value for reference. OPEC can be implemented to adapt to this situation and improve clustering quality. By understanding the changes of the users’ power consumption modes, we can detect abnormal power consumption behaviors, and also analyze the changing trend to improve the operations of the power system. This is significant for the regulation of peak load in the power grid. In addition, it can bring certain economic benefits to the operation of the power grid.

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Low-rank Estimation Based Evolutionary Clustering for Community Detection in Temporal Networks

Cited by 4 publications

References 19 publications

An Implementation of the HDBSCAN* Clustering Algorithm

An Implementation of the HDBSCAN* Clustering Algorithm

Robust Community Detection in Graphs

OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering

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