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Results 1 - 10
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334
Clustering partially observed graphs via convex optimization.
- Journal of Machine Learning Research,
, 2014
"... Abstract This paper considers the problem of clustering a partially observed unweighted graph-i.e., one where for some node pairs we know there is an edge between them, for some others we know there is no edge, and for the remaining we do not know whether or not there is an edge. We want to organiz ..."
Abstract
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Cited by 47 (13 self)
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Abstract This paper considers the problem of clustering a partially observed unweighted graph-i.e., one where for some node pairs we know there is an edge between them, for some others we know there is no edge, and for the remaining we do not know whether or not there is an edge. We want
Focused clustering and outlier detection in large attributed graphs
- In ACM Special Interest Group on Knowledge Discovery and Data Mining (SIG-KDD
, 2014
"... Graph clustering and graph outlier detection have been stud-ied extensively on plain graphs, with various applications. Recently, algorithms have been extended to graphs with at-tributes as often observed in the real-world. However, all of these techniques fail to incorporate the user preference int ..."
Abstract
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Cited by 4 (2 self)
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Graph clustering and graph outlier detection have been stud-ied extensively on plain graphs, with various applications. Recently, algorithms have been extended to graphs with at-tributes as often observed in the real-world. However, all of these techniques fail to incorporate the user preference
Efficient Aggregation for Graph Summarization
"... Graphs are widely used to model real world objects and their relationships, and large graph datasets are common in many application domains. To understand the underlying characteristics of large graphs, graph summarization techniques are critical. However, existing graph summarization methods are mo ..."
Abstract
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Cited by 83 (5 self)
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-style aggregation methods that allow users to drill-down or roll-up to control the resolution of summarization, our methods provide an analogous functionality for large graph datasets. The first operation, called SNAP, produces a summary graph by grouping nodes based on user-selected node attributes
Graph Attribute Embedding via Riemannian Submersion Learning
"... In this paper, we tackle the problem of embedding a set of relational structures into a metric space for purposes of matching and categorisation. To this end, we view the problem from a Riemannian perspective and make use of the concepts of charts on the manifold to define the embedding as a mixture ..."
Abstract
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Cited by 1 (1 self)
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mixture of class-specific submersions. Formulated in this manner, the mixture weights are recovered using a probability density estimation on the embedded graph node coordinates. Further, we recover these class-specific submersions making use of an iterative trust-region method so as to minimise the L2
Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies
"... Abstract. We present a simple technique for detecting communities by utilizing both the link structure and folksonomy (or tag) information. A simple way to describe our approach is by defining a community as a set of nodes in a graph that link more frequently within this set than outside it and they ..."
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Cited by 5 (0 self)
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Abstract. We present a simple technique for detecting communities by utilizing both the link structure and folksonomy (or tag) information. A simple way to describe our approach is by defining a community as a set of nodes in a graph that link more frequently within this set than outside
Utilization of constrained spectral clustering for clustering of graph nodes containing record data *
"... Abstract Clustering is one of the most common and most widely used methods of data mining. Many clustering algorithms can be utilized for various purposes, but most of these methods can only deal with one data type at a time. There are few methods in existence that can deal with data of different o ..."
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origins and/or different types simultaneously, but most of these are iterative However, by using the method of constrained spectral clustering, which was originally established to define graph nodes that surely fall into the same cluster or surely not, we can incorporate the influence of the similarities
Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment
"... Abstract—Network clustering is an important problem that has recently drawn a lot of attentions. Most existing work focuses on clustering nodes within a single network. In many applications, however, there exist multiple related networks, in which each network may be constructed from a different dom ..."
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Abstract—Network clustering is an important problem that has recently drawn a lot of attentions. Most existing work focuses on clustering nodes within a single network. In many applications, however, there exist multiple related networks, in which each network may be constructed from a different
Using Bi-modal Alignment and Clustering Techniques for Documents and Speech Thematic Segmentations
- in Thirteenth Conference on Information and Knowledge Management CIKM 2004
, 2004
"... In this paper, we describe a new method for a simultaneous thematic segmentation of the meeting dialogs and the documents discussed or visible throughout the meeting. This bi-modal method is suitable for multimodal applications that are centered on documents, such as meetings and lectures, where doc ..."
Abstract
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Cited by 9 (5 self)
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documents can be aligned with meeting dialogs. Bringing into play this alignment, our bi-modal segmentation method first transforms its results into a set of nodes in a 2D graph space, where the two axes represent respectively the document units and the meeting dialogs units. Secondly, via a clustering
Visual inspection of multivariate graphs
- Computer Graphics Forum (Proc. EuroVis
"... Most graph visualization techniques focus on the structure of graphs and do not offer support for dealing with node attributes and edge labels. To enable users to detect relations and patterns in terms of data associated with nodes and edges, we present a technique where this data plays a more centr ..."
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Cited by 16 (2 self)
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central role. Nodes and edges are clustered based on associated data. Via direct manipulation users can interactively inspect and query the graph. Questions that can be answered include, “which edge types are activated by specific node attributes? ” and, “how and from where can I reach specific types
An Effective Comparison of Graph Clustering Algorithms via Random Graphs
"... Many graph clustering algorithms have been proposed in recent past researches, each algorithm having its own advantages and drawbacks. All these algorithms rely on a very different approach so it’s really hard to say that which one is the most efficient and optimal if we talk in the sense of perform ..."
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Cited by 1 (0 self)
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based on Erdos-Renyi and Power-Law Distribution graphs. The basic parameters used for comparison are Edge Density, Run Time, Number of Nodes, Cluster Size and Singleton Cluster. Our approach is an effective one because firstly we have used two types of graph generators, Erdos-Renyi and Scaled
Results 1 - 10
of
334
