Large graph analysis in the g mine system
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Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
Highlighted notes on Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds.
While doing research work under Prof. Kishore Kothapalli.
Laxman Dhulipala, David Durfee, Janardhan Kulkarni, Richard Peng, Saurabh Sawlani, Xiaorui Sun:
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds. SODA 2020: 1300-1319
In this paper we study the problem of dynamically maintaining graph properties under batches of edge insertions and deletions in the massively parallel model of computation. In this setting, the graph is stored on a number of machines, each having space strongly sublinear with respect to the number of vertices, that is, n for some constant 0 < < 1. Our goal is to handle batches of updates and queries where the data for each batch fits onto one machine in constant rounds of parallel computation, as well as to reduce the total communication between the machines. This objective corresponds to the gradual buildup of databases over time, while the goal of obtaining constant rounds of communication for problems in the static setting has been elusive for problems as simple as undirected graph connectivity. We give an algorithm for dynamic graph connectivity in this setting with constant communication rounds and communication cost almost linear in terms of the batch size. Our techniques combine a new graph contraction technique, an independent random sample extractor from correlated samples, as well as distributed data structures supporting parallel updates and queries in batches. We also illustrate the power of dynamic algorithms in the MPC model by showing that the batched version of the adaptive connectivity problem is P-complete in the centralized setting, but sub-linear sized batches can be handled in a constant number of rounds. Due to the wide applicability of our approaches, we believe it represents a practically-motivated workaround to the current difficulties in designing more efficient massively parallel static graph algorithms.
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GMine is a framework for analyzing large graphs with hundreds of thousands of nodes and millions of edges. It integrates a hierarchical graph representation called SuperGraph and Graph-Tree and a graph summarization methodology called CEPS. This allows graphs to be investigated globally and locally by grasping the neighborhood of a single node or group of nodes with sublinear complexity. GMine was developed as a proof of concept visual environment for interactively exploring large graphs.
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directions for future research.
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
In this paper we study the problem of dynamically
maintaining graph properties under batches of edge
insertions and deletions in the massively parallel model
of computation. In this setting, the graph is stored
on a number of machines, each having space strongly
sublinear with respect to the number of vertices, that
is, n
for some constant 0 < < 1. Our goal is to
handle batches of updates and queries where the data
for each batch fits onto one machine in constant rounds
of parallel computation, as well as to reduce the total
communication between the machines. This objective
corresponds to the gradual buildup of databases over
time, while the goal of obtaining constant rounds of
communication for problems in the static setting has
been elusive for problems as simple as undirected graph
connectivity.
We give an algorithm for dynamic graph connectivity
in this setting with constant communication rounds and
communication cost almost linear in terms of the batch
size. Our techniques combine a new graph contraction
technique, an independent random sample extractor from
correlated samples, as well as distributed data structures
supporting parallel updates and queries in batches.
We also illustrate the power of dynamic algorithms in
the MPC model by showing that the batched version
of the adaptive connectivity problem is P-complete in
the centralized setting, but sub-linear sized batches can
be handled in a constant number of rounds. Due to
the wide applicability of our approaches, we believe
it represents a practically-motivated workaround to the
current difficulties in designing more efficient massively
parallel static graph algorithms.
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
Highlighted notes on Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds.
While doing research work under Prof. Kishore Kothapalli.
Laxman Dhulipala, David Durfee, Janardhan Kulkarni, Richard Peng, Saurabh Sawlani, Xiaorui Sun:
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds. SODA 2020: 1300-1319
In this paper we study the problem of dynamically maintaining graph properties under batches of edge insertions and deletions in the massively parallel model of computation. In this setting, the graph is stored on a number of machines, each having space strongly sublinear with respect to the number of vertices, that is, n for some constant 0 < < 1. Our goal is to handle batches of updates and queries where the data for each batch fits onto one machine in constant rounds of parallel computation, as well as to reduce the total communication between the machines. This objective corresponds to the gradual buildup of databases over time, while the goal of obtaining constant rounds of communication for problems in the static setting has been elusive for problems as simple as undirected graph connectivity. We give an algorithm for dynamic graph connectivity in this setting with constant communication rounds and communication cost almost linear in terms of the batch size. Our techniques combine a new graph contraction technique, an independent random sample extractor from correlated samples, as well as distributed data structures supporting parallel updates and queries in batches. We also illustrate the power of dynamic algorithms in the MPC model by showing that the batched version of the adaptive connectivity problem is P-complete in the centralized setting, but sub-linear sized batches can be handled in a constant number of rounds. Due to the wide applicability of our approaches, we believe it represents a practically-motivated workaround to the current difficulties in designing more efficient massively parallel static graph algorithms.
Learning Graph Representation for Data-Efficiency RL
This document provides information about Laura Toni's presentation on learning graph representation for data-efficient reinforcement learning. It discusses Laura Toni's affiliation with the LASP Research group at University College London, which focuses on machine learning, signal processing, and developing strategies for large-scale networks exploiting graph structures. The key goal is to exploit graph structure to develop efficient learning algorithms. The document lists some applications such as virtual reality systems, bandit problems, structural reinforcement learning, and influence maximization.
Fashion AI
This document summarizes a research paper on Fashion AI. It proposes a new Group Decreasing Network (GroupDNet) that uses group convolutions in the generator and gradually reduces the percentage of groups in the decoder's convolutions. This allows the model to have more control over generating images from semantic labels and produce high-quality, multi-modal outputs. The paper describes GroupDNet's architecture, compares it to other approaches like using multiple generators, and shows it outperforms other methods on challenging datasets based on metrics like FID and mIoU. Potential applications discussed include mixed fashion styles, semantic manipulation, and tracking fashion trends over time. The conclusion discusses GroupDNet's performance but notes room for improving computational efficiency
Practical Parallel Hypergraph Algorithms | PPoPP ’20
This paper presents efficient parallel algorithms for hypergraph processing implemented in a new framework called Hygra. Hygra extends the Ligra graph processing framework to support hypergraphs. It represents hypergraphs using a bipartite graph and implements optimizations from Ligra. The paper introduces parallel hypergraph algorithms for betweenness centrality, maximal independent set, k-core decomposition, hypertrees, hyperpaths, connected components, PageRank, and single-source shortest paths. Experiments show the algorithms in Hygra achieve good parallel speedup and outperform existing hypergraph frameworks.
DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES
https://gist.github.com/wolfram77/54c4a14d9ea547183c6c7b3518bf9cd1
There exist a number of dynamic graph generators. Barbasi-Albert model iteratively attach new vertices to pre-exsiting vertices in the graph using preferential attachment (edges to high degree vertices are more likely - rich get richer - Pareto principle). However, graph size increases monotonically, and density of graph keeps increasing (sparsity decreasing).
Gorke's model uses a defined clustering to uniformly add vertices and edges. Purohit's model uses motifs (eg. triangles) to mimick properties of existing dynamic graphs, such as growth rate, structure, and degree distribution. Kronecker graph generators are used to increase size of a given graph, with power-law distribution.
To generate dynamic graphs, we must choose a metric to compare two graphs. Common metrics include diameter, clustering coefficient (modularity?), triangle counting (triangle density?), and degree distribution.
In this paper, the authors propose Dygraph, a dynamic graph generator that uses degree distribution as the only metric. The authors observe that many real-world graphs differ from the power-law distribution at the tail end. To address this issue, they propose binning, where the vertices beyond a certain degree (minDeg = min(deg) s.t. |V(deg)| < H, where H~10 is the number of vertices with a given degree below which are binned) are grouped into bins of degree-width binWidth, max-degree localMax, and number of degrees in bin with at least one vertex binSize (to keep track of sparsity). This helps the authors to generate graphs with a more realistic degree distribution.
The process of generating a dynamic graph is as follows. First the difference between the desired and the current degree distribution is calculated. The authors then create an edge-addition set where each vertex is present as many times as the number of additional incident edges it must recieve. Edges are then created by connecting two vertices randomly from this set, and removing both from the set once connected. Currently, authors reject self-loops and duplicate edges. Removal of edges is done in a similar fashion.
Authors observe that adding edges with power-law properties dominates the execution time, and consider parallelizing DyGraph as part of future work.
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
This document summarizes a research paper on learning graph representations for deep divergence graph kernels (DDGK). DDGK learns graph representations without supervision or domain knowledge by using a node-to-edges encoder and isomorphism attention. The isomorphism attention provides a bidirectional mapping between nodes in two graphs. DDGK then calculates a divergence score between the source and target graphs as a measure of their (dis)similarity. Experimental results showed DDGK produces representations competitive with other graph kernel baselines. The paper proposes several extensions, including different graph encoders and attention mechanisms, as well as improved regularization and scalability.
Gc2005vk
This document describes an interactive multi-label image segmentation algorithm called "GrowCut" based on cellular automata. The algorithm can segment N-dimensional images with multiple labels. With modest user input of labeled pixels, GrowCut automatically segments the rest of the image in an iterative process. It requires less user effort than other techniques for moderately difficult images. The algorithm has advantages such as efficiency, parallelizability, and extensibility to generate new segmentation methods.
GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspec...
GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspec...Konstantinos Demertzis
The document discusses a new meta-ensemble zero-shot learning method called MAME-ZsL for hyperspectral image analysis and classification. MAME-ZsL overcomes the difficulties of traditional deep learning methods that require large labeled datasets and long training times. It reduces computational costs, avoids overfitting, and achieves high classification accuracy even when testing classes were not present during training. The method is a novel optimization-based meta-ensemble architecture that facilitates learning representations from limited labeled examples to enable one-shot and zero-shot learning.Similar to Large graph analysis in the g mine system (20)
PARTITIONING WIDE AREA GRAPHS USING A SPACE FILLING CURVE
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Big Graph : Tools, Techniques, Issues, Challenges and Future Directions
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BIG GRAPH: TOOLS, TECHNIQUES, ISSUES, CHALLENGES AND FUTURE DIRECTIONS
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Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds
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Learning Graph Representation for Data-Efficiency RL
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Practical Parallel Hypergraph Algorithms | PPoPP ’20
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DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES
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DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
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GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspec...
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Large graph analysis in the g mine system
- 1. LARGE GRAPH ANALYSIS IN THE GMINE SYSTEM ABSTRACT: Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers, and communities. These tasks are better performed in an interactive environment, where human expertise can guide the process. For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend. We propose an innovative framework suited for any kind of tree-like graph visual design. GMine integrates 1) a representation for graphs organized as hierarchies of partitions—the concepts of SuperGraph and Graph-Tree; and 2) a graph summarization methodology—CEPS. Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click. As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally. ECWAY TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE CELL: +91 98949 17187, +91 875487 2111 / 3111 / 4111 / 5111 / 6111 VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com