The Experts below are selected from a list of 72 Experts worldwide ranked by ideXlab platform

Christos Faloutsos - One of the best experts on this subject based on the ideXlab platform.

  • ICDM - CoreScope: Graph Mining Using k-Core Analysis — Patterns, Anomalies and Algorithms
    2016 IEEE 16th International Conference on Data Mining (ICDM), 2016
    Co-Authors: Kijung Shin, Tina Eliassi-rad, Christos Faloutsos
    Abstract:

    How do the k-core structures of real-world graphs look like? What are the common patterns and the anomalies? How can we use them for Algorithm design and applications? A k-core is the maximal subgraph where all vertices have degree at least k. This concept has been applied to such diverse areas as hierarchical structure analysis, graph visualization, and graph clustering. Here, we explore pervasive patterns that are related to k-cores and emerging in graphs from several diverse domains. Our discoveries are as follows: (1) Mirror Pattern: coreness of vertices (i.e., maximum k such that each vertex belongs to the k-core) is strongly correlated to their degree. (2) Core-Triangle Pattern: degeneracy of a graph (i.e., maximum k such that the k-core exists in the graph) obeys a 3-to-1 power law with respect to the count of triangles. (3) Structured Core Pattern: degeneracy-cores are not cliques but have non-trivial structures such as core-periphery and communities. Our Algorithmic contributions show the usefulness of these patterns. (1) Core-A, which measures the deviation from Mirror Pattern, successfully finds anomalies in real-world graphs complementing densest-subgraph based anomaly detection methods. (2) Core-D, a single-pass streaming Algorithm based on Core-Triangle Pattern, accurately estimates the degeneracy of billion-scale graphs up to 7× faster than a recent Multipass Algorithm.(3) Core-S, inspired by Structured Core Pattern, identifies influential spreaders up to 17× faster than top competitors with comparable accuracy.

  • CoreScope: Graph Mining Using k-Core Analysis — Patterns, Anomalies and Algorithms
    2016 IEEE 16th International Conference on Data Mining (ICDM), 2016
    Co-Authors: Kijung Shin, Tina Eliassi-rad, Christos Faloutsos
    Abstract:

    How do the k-core structures of real-world graphs look like? What are the common patterns and the anomalies? How can we use them for Algorithm design and applications? A k-core is the maximal subgraph where all vertices have degree at least k. This concept has been applied to such diverse areas as hierarchical structure analysis, graph visualization, and graph clustering. Here, we explore pervasive patterns that are related to k-cores and emerging in graphs from several diverse domains. Our discoveries are as follows: (1) Mirror Pattern: coreness of vertices (i.e., maximum k such that each vertex belongs to the k-core) is strongly correlated to their degree. (2) Core-Triangle Pattern: degeneracy of a graph (i.e., maximum k such that the k-core exists in the graph) obeys a 3-to-1 power law with respect to the count of triangles. (3) Structured Core Pattern: degeneracy-cores are not cliques but have non-trivial structures such as core-periphery and communities. Our Algorithmic contributions show the usefulness of these patterns. (1) Core-A, which measures the deviation from Mirror Pattern, successfully finds anomalies in real-world graphs complementing densest-subgraph based anomaly detection methods. (2) Core-D, a single-pass streaming Algorithm based on Core-Triangle Pattern, accurately estimates the degeneracy of billion-scale graphs up to 7× faster than a recent Multipass Algorithm.(3) Core-S, inspired by Structured Core Pattern, identifies influential spreaders up to 17× faster than top competitors with comparable accuracy.

Kijung Shin - One of the best experts on this subject based on the ideXlab platform.

  • ICDM - CoreScope: Graph Mining Using k-Core Analysis — Patterns, Anomalies and Algorithms
    2016 IEEE 16th International Conference on Data Mining (ICDM), 2016
    Co-Authors: Kijung Shin, Tina Eliassi-rad, Christos Faloutsos
    Abstract:

    How do the k-core structures of real-world graphs look like? What are the common patterns and the anomalies? How can we use them for Algorithm design and applications? A k-core is the maximal subgraph where all vertices have degree at least k. This concept has been applied to such diverse areas as hierarchical structure analysis, graph visualization, and graph clustering. Here, we explore pervasive patterns that are related to k-cores and emerging in graphs from several diverse domains. Our discoveries are as follows: (1) Mirror Pattern: coreness of vertices (i.e., maximum k such that each vertex belongs to the k-core) is strongly correlated to their degree. (2) Core-Triangle Pattern: degeneracy of a graph (i.e., maximum k such that the k-core exists in the graph) obeys a 3-to-1 power law with respect to the count of triangles. (3) Structured Core Pattern: degeneracy-cores are not cliques but have non-trivial structures such as core-periphery and communities. Our Algorithmic contributions show the usefulness of these patterns. (1) Core-A, which measures the deviation from Mirror Pattern, successfully finds anomalies in real-world graphs complementing densest-subgraph based anomaly detection methods. (2) Core-D, a single-pass streaming Algorithm based on Core-Triangle Pattern, accurately estimates the degeneracy of billion-scale graphs up to 7× faster than a recent Multipass Algorithm.(3) Core-S, inspired by Structured Core Pattern, identifies influential spreaders up to 17× faster than top competitors with comparable accuracy.

  • CoreScope: Graph Mining Using k-Core Analysis — Patterns, Anomalies and Algorithms
    2016 IEEE 16th International Conference on Data Mining (ICDM), 2016
    Co-Authors: Kijung Shin, Tina Eliassi-rad, Christos Faloutsos
    Abstract:

    How do the k-core structures of real-world graphs look like? What are the common patterns and the anomalies? How can we use them for Algorithm design and applications? A k-core is the maximal subgraph where all vertices have degree at least k. This concept has been applied to such diverse areas as hierarchical structure analysis, graph visualization, and graph clustering. Here, we explore pervasive patterns that are related to k-cores and emerging in graphs from several diverse domains. Our discoveries are as follows: (1) Mirror Pattern: coreness of vertices (i.e., maximum k such that each vertex belongs to the k-core) is strongly correlated to their degree. (2) Core-Triangle Pattern: degeneracy of a graph (i.e., maximum k such that the k-core exists in the graph) obeys a 3-to-1 power law with respect to the count of triangles. (3) Structured Core Pattern: degeneracy-cores are not cliques but have non-trivial structures such as core-periphery and communities. Our Algorithmic contributions show the usefulness of these patterns. (1) Core-A, which measures the deviation from Mirror Pattern, successfully finds anomalies in real-world graphs complementing densest-subgraph based anomaly detection methods. (2) Core-D, a single-pass streaming Algorithm based on Core-Triangle Pattern, accurately estimates the degeneracy of billion-scale graphs up to 7× faster than a recent Multipass Algorithm.(3) Core-S, inspired by Structured Core Pattern, identifies influential spreaders up to 17× faster than top competitors with comparable accuracy.

Jeremy H Wright - One of the best experts on this subject based on the ideXlab platform.

  • Multipass Algorithm for acquisition of salient acoustic morphemes
    Conference of the International Speech Communication Association, 2001
    Co-Authors: Michael Levit, Allen L Gorin, Jeremy H Wright
    Abstract:

    We are interested in spoken language understanding within the domain of automated telecommunication services. Our current methodology involves training statistical language models from large annotated corpora for recognition and understanding. Since the transcribing of large speech corpora is a resource consuming task, we are motivated to exploit speech without transcriptions. In particular, we learn the semantic associations for a task exploiting only phone-based sequences from the output of a task-independent ASR-system. In this paper we present a new Multipass Algorithm for acquiring salient phone sequences from untranscribed speech corpora and evaluate their utility for the HMIHY task. Compared to our previous strategy, this Algorithm is shown to produce improved call-classification results while reducing up to 7-fold the number of salient phone-sequences selected for training.

  • INTERSPEECH - Multipass Algorithm for acquisition of salient acoustic morphemes.
    2001
    Co-Authors: Michael Levit, Allen L Gorin, Jeremy H Wright
    Abstract:

    We are interested in spoken language understanding within the domain of automated telecommunication services. Our current methodology involves training statistical language models from large annotated corpora for recognition and understanding. Since the transcribing of large speech corpora is a resource consuming task, we are motivated to exploit speech without transcriptions. In particular, we learn the semantic associations for a task exploiting only phone-based sequences from the output of a task-independent ASR-system. In this paper we present a new Multipass Algorithm for acquiring salient phone sequences from untranscribed speech corpora and evaluate their utility for the HMIHY task. Compared to our previous strategy, this Algorithm is shown to produce improved call-classification results while reducing up to 7-fold the number of salient phone-sequences selected for training.

Tina Eliassi-rad - One of the best experts on this subject based on the ideXlab platform.

  • ICDM - CoreScope: Graph Mining Using k-Core Analysis — Patterns, Anomalies and Algorithms
    2016 IEEE 16th International Conference on Data Mining (ICDM), 2016
    Co-Authors: Kijung Shin, Tina Eliassi-rad, Christos Faloutsos
    Abstract:

    How do the k-core structures of real-world graphs look like? What are the common patterns and the anomalies? How can we use them for Algorithm design and applications? A k-core is the maximal subgraph where all vertices have degree at least k. This concept has been applied to such diverse areas as hierarchical structure analysis, graph visualization, and graph clustering. Here, we explore pervasive patterns that are related to k-cores and emerging in graphs from several diverse domains. Our discoveries are as follows: (1) Mirror Pattern: coreness of vertices (i.e., maximum k such that each vertex belongs to the k-core) is strongly correlated to their degree. (2) Core-Triangle Pattern: degeneracy of a graph (i.e., maximum k such that the k-core exists in the graph) obeys a 3-to-1 power law with respect to the count of triangles. (3) Structured Core Pattern: degeneracy-cores are not cliques but have non-trivial structures such as core-periphery and communities. Our Algorithmic contributions show the usefulness of these patterns. (1) Core-A, which measures the deviation from Mirror Pattern, successfully finds anomalies in real-world graphs complementing densest-subgraph based anomaly detection methods. (2) Core-D, a single-pass streaming Algorithm based on Core-Triangle Pattern, accurately estimates the degeneracy of billion-scale graphs up to 7× faster than a recent Multipass Algorithm.(3) Core-S, inspired by Structured Core Pattern, identifies influential spreaders up to 17× faster than top competitors with comparable accuracy.

  • CoreScope: Graph Mining Using k-Core Analysis — Patterns, Anomalies and Algorithms
    2016 IEEE 16th International Conference on Data Mining (ICDM), 2016
    Co-Authors: Kijung Shin, Tina Eliassi-rad, Christos Faloutsos
    Abstract:

    How do the k-core structures of real-world graphs look like? What are the common patterns and the anomalies? How can we use them for Algorithm design and applications? A k-core is the maximal subgraph where all vertices have degree at least k. This concept has been applied to such diverse areas as hierarchical structure analysis, graph visualization, and graph clustering. Here, we explore pervasive patterns that are related to k-cores and emerging in graphs from several diverse domains. Our discoveries are as follows: (1) Mirror Pattern: coreness of vertices (i.e., maximum k such that each vertex belongs to the k-core) is strongly correlated to their degree. (2) Core-Triangle Pattern: degeneracy of a graph (i.e., maximum k such that the k-core exists in the graph) obeys a 3-to-1 power law with respect to the count of triangles. (3) Structured Core Pattern: degeneracy-cores are not cliques but have non-trivial structures such as core-periphery and communities. Our Algorithmic contributions show the usefulness of these patterns. (1) Core-A, which measures the deviation from Mirror Pattern, successfully finds anomalies in real-world graphs complementing densest-subgraph based anomaly detection methods. (2) Core-D, a single-pass streaming Algorithm based on Core-Triangle Pattern, accurately estimates the degeneracy of billion-scale graphs up to 7× faster than a recent Multipass Algorithm.(3) Core-S, inspired by Structured Core Pattern, identifies influential spreaders up to 17× faster than top competitors with comparable accuracy.

D.p. Greenberg - One of the best experts on this subject based on the ideXlab platform.

  • Accurate direct illumination using iterative adaptive sampling
    IEEE Transactions on Visualization and Computer Graphics, 2006
    Co-Authors: M. Donikian, B. Walter, Kavita Bala, S. Fernandez, D.p. Greenberg
    Abstract:

    This paper introduces a new Multipass Algorithm for efficiently computing direct illumination in scenes with many lights and complex occlusion. Images are first divided into 8times8 pixel blocks and for each point to be shaded within a block, a probability density function (PDF) is constructed over the lights and sampled to estimate illumination using a small number of shadow rays. Information from these samples is then aggregated at both the pixel and block level and used to optimize the PDFs for the next pass. Over multiple passes the PDFs and pixel estimates are updated until convergence. Using aggregation and feedback progressively improves the sampling and automatically exploits both visibility and spatial coherence. We also use novel extensions for efficient antialiasing. Our adaptive Multipass approach computes accurate direct illumination eight times faster than prior approaches in tests on several complex scenes