Evolutionary spectral clustering by incorporating temporal smoothness

By Yun Chi, Xiaodan Song, Dengyong Zhou, K. Hino, Belle L. Tseng. Knowledge Discovery and Data Mining, 2007

413 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ca122cd2ba92e722bc4255c456129d9daaabe535

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Citations in the corpus

Community detection in networks: A user guide (2016, background): “Some interesting algorithms based on evolutionary clustering have been proposed (Chi et al., 2007; Lin et al., 2008).”
A survey on data stream clustering and classification (2015, background): “proposed two frameworks for evolutionary spectral clustering, called Preserving Cluster Quality (PCQ) and Preserving Cluster Membership (PCM) [25,26].”
Multitask Bregman clustering (2010, methodology): “Then based on the general Bregman divergences, the within-task loss is defined as the average Bregman divergence from a data sample to its cluster centroid.”
Unifying Virtual and Physical Worlds (2017, methodology): “As discussed in Section 3.2, the objective functions of clustering over an individual network can be expressed in the form of tr ( G v L v G v ) and tr ( G p L v G p ), where L v and L p are the normalized Laplacian matrices deﬁned based on the afﬁnity matrices A v and A p in Equation (1),…”
Tracking the Evolution of Communities in Dynamic Social Networks (2010, background): “Evolutionary versions of common spectral clustering algorithms have also been proposed [7].”
An event-based framework for characterizing the evolutionary behavior of interaction graphs (2007, methodology): “Chi et al. [2007] have developed an evolutionary version of the spectral clustering algorithm making use of temporal smoothness, to obtain consistent clusters.”
Attributed Network Embedding for Learning in a Dynamic Environment (2017, background): “e proposed online embedding model is motivated by the observation that most of real-world networks, with no exception for aributed networks, oen evolve smoothly in the temporal dimension between two consecutive time steps [2, 10].”
Event Detection in Social Streams (2012, methodology): “Some clustering methods have recently also been designed for dynamic networks [8, 9], though they do not use the content of the underlying network for the mining process.”
Community detection in graphs (2009, methodology): “[387] designed two implementations for spectral clustering (Section 4.”
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks (2008, background): “[3] extended similar ideas and proposed the first evolutionary spectral clustering algorithms.”
TextFlow: Towards Better Understanding of Evolving Topics in Text (2011, background): “Evolutionary clustering [5] [6] [31] [32] [34] focuses the problem of processing temporal data to produce a sequence of clusters.”
Outlier Analysis (2013, methodology): “While evolutionary clustering [99, 116] is widely studied in the community detection literature, a recent method [192] also inte-”
Detecting communities and their evolutions in dynamic social networks—a Bayesian approach (2011, methodology): “This blog dataset was collected by the NEC Labs and have been used in several previous studies on dynamic social networks (Chi et al. 2007; Lin et al. 2008).”