2016年3月22日火曜日

Graph @ SIGMOD



  • Andrew McGregor: Graph stream algorithms: a survey. SIGMOD Record 43(1): 9-20 (2014)
  • Renzo Angles, Peter A. Boncz, Josep-Lluis Larriba-Pey, Irini Fundulaki, Thomas Neumann, Orri Erling, Peter Neubauer, Norbert Martínez-Bazan, Venelin Kotsev, Ioan Toma: The linked data benchmark council: a graph and RDF industry benchmarking effort. SIGMOD Record 43(1): 27-31 (2014)
  • Jianlong Zhong, Bingsheng He: Medusa: A Parallel Graph Processing System on Graphics Processors. SIGMOD Record 43(2): 35-40 (2014)
  • Jungeun Kim, Jae-Gil Lee: Community Detection in Multi-Layer Graphs: A Survey. SIGMOD Record 44(3): 37-48 (2015)
  • Takumi Nito, Yoshiko Nagasaka, Hiroshi Uchigaito: Large-Scale BSP Graph Processing in Distributed Non-Volatile Memory. GRADES@SIGMOD/PODS 2015: 1:1-1:6
  • Dung T. Nguyen, Molham Aref, Martin Bravenboer, George Kollias, Hung Q. Ngo, Christopher Ré, Atri Rudra: Join Processing for Graph Patterns: An Old Dog with New Tricks. GRADES@SIGMOD/PODS 2015: 2:1-2:8
  • Ioanna Filippidou, Yannis Kotidis: Online Partitioning of Multi-Labeled Graphs. GRADES@SIGMOD/PODS 2015: 3:1-3:6
  • Nathan Hawes, Ben Barham, Cristina Cifuentes: Frappé: Querying the Linux Kernel Dependency Graph. GRADES@SIGMOD/PODS 2015: 4:1-4:6
  • Oshini Goonetilleke, Saket Sathe, Timos K. Sellis, Xiuzhen Zhang: Microblogging Queries on Graph Databases: An Introspection. GRADES@SIGMOD/PODS 2015: 5:1-5:6
  • Joan Guisado-Gámez, Arnau Prat-Pérez: Understanding Graph Structure of Wikipedia for Query Expansion. GRADES@SIGMOD/PODS 2015: 6:1-6:6
  • Mihai Capota, Tim Hegeman, Alexandru Iosup, Arnau Prat-Pérez, Orri Erling, Peter A. Boncz: Graphalytics: A Big Data Benchmark for Graph-Processing Platforms. GRADES@SIGMOD/PODS 2015: 7:1-7:6
  • Shahan Khatchadourian, Mariano P. Consens: Constructing Bisimulation Summaries on a Multi-Core Graph Processing Framework. GRADES@SIGMOD/PODS 2015: 8:1-8:7
  • Qi Fan, Kian-Lee Tan: Towards Window Analytics over Large-scale Graphs. SIGMOD PhD Symposium 2015: 15-19
  • Antonio Maccioni: Flexible Query Answering over Graph-modeled Data. SIGMOD PhD Symposium 2015: 27-32
  • Andrew McGregor: Graph stream algorithms: a survey. SIGMOD Record 43(1): 9-20 (2014)
  • Renzo Angles, Peter A. Boncz, Josep-Lluis Larriba-Pey, Irini Fundulaki, Thomas Neumann, Orri Erling, Peter Neubauer, Norbert Martínez-Bazan, Venelin Kotsev, Ioan Toma: The linked data benchmark council: a graph and RDF industry benchmarking effort. SIGMOD Record 43(1): 27-31 (2014)
  • Jianlong Zhong, Bingsheng He: Medusa: A Parallel Graph Processing System on Graphics Processors. SIGMOD Record 43(2): 35-40 (2014)
  • Matteo Lissandrini, Davide Mottin, Themis Palpanas, Dimitra Papadimitriou, Yannis Velegrakis: Unleashing the Power of Information Graphs. SIGMOD Record 43(4): 21-26 (2014)
  • Michael D. Lieberman, Sutanay Choudhury, Marisa Hughes, Dennis Patrone, Robert T. Hider Jr., Christine D. Piatko, Matthew Chapman, J. P. Marple, David Silberberg: Parasol: An Architecture for Cross-Cloud Federated Graph Querying. DanaC@SIGMOD 2014: 4:1-4:4




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Graph @ Supercomputing

2015 

 BD-CATS: big data clustering at trillion particle scale. 6:1-6:12
Performance optimization for the k-nearest neighbors kernel on x86 architectures. 7:1-7:12
A parallel connectivity algorithm for de Bruijn graphs in metagenomic applications. 
Exploring network optimizations for large-scale graph analytics
GossipMap: a distributed community detection algorithm for billion-edge directed graphs
GraphReduce: processing large-scale graphs on accelerator-based systems
Data partitioning strategies for graph workloads on heterogeneous clusters
Scaling iterative graph computations with GraphMap
PGX.D: a fast distributed graph processing engine
A work-efficient algorithm for parallel unordered depth-first search
Enterprise: breadth-first graph traversal on GPUs
GraphBIG: understanding graph computing in the context of industrial solutions

 2014 
Fast Iterative Graph Computation: A Path Centric ApproachParallel De Bruijn Graph Construction and Traversal for De Novo Genome Assembly
Faster Parallel Traversal of Scale Free Graphs at Extreme Scale with Vertex DelegatesPardicle: Parallel Approximate Density-Based ClusteringScalable and High Performance Betweenness Centrality on the GPU
Fast Sparse Matrix-Vector Multiplication on GPUs for Graph Applications

 2013
Efficient data partitioning model for heterogeneous graphs in the cloud
Scalable parallel OPTICS data clustering using graph algorithmic techniques
Scalable matrix computations on large scale-free graphs using 2D graph partitioning
Scalable parallel graph partitioning.
On fast parallel detection of strongly connected components (SCC) in small-world graphs

2012
Direction-optimizing breadth-first search
Breaking the speed and scalability barriers for graph exploration on distributed-memory machines.
Large-scale energy-efficient graph traversal: a path to efficient data-intensive supercomputing
A new scalable parallel DBSCAN algorithm using the disjoint-set data structure
Parallel Bayesian network structure learning with application to gene networks
A multithreaded algorithm for network alignment via approximate matching.
NUMA-aware graph mining techniques for performance and energy efficiency
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Graph @ VLDB



2011

  • On Triangulation-based Dense Neighborhood Graphs Discovery.
  • Graph Indexing of Road Networks for Shortest Path Queries with Label Restrictions.
  • Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining.
  • Human-assisted graph search: it's okay to ask questions.
  • High-throughput transaction executions on graphics processors.
  • Efficient Parallel Lists Intersection and Index Compression Algorithms using Graphics Processing Units.
  • gStore: Answering SPARQL Queries via Subgraph Matching.
  • Surrogate Parenthood: Protected and Informative Graphs.
  • Distance-Constraint Reachability Computation in Uncertain Graphs.
  • Keyword Search in Graphs: Finding r-cliques.
  • On Querying Historical Evolving Graph Sequences.
  • Efficient Subgraph Search over Large Uncertain Graphs.
  • Scalable SPARQL Querying of Large RDF Graphs.
  • Private Analysis of Graph Structure.
  • Graph Data Management Systems for New Application Domains.


2012
  • gSketch: On Query Estimation in Graph Streams.
  • Capturing Topology in Graph Pattern Matching.
  • Relational Approach for Shortest Path Discovery over Large Graphs.
  • Densest Subgraph in Streaming and MapReduce.
  • Mining Attribute-structure Correlated Patterns in Large Attributed Graphs.
  • Dense Subgraph Maintenance under Streaming Edge Weight Updates for Real-time Story Identification.
  • Distributed GraphLab: A Framework for Machine Learning in the Cloud.
  • Adding Logical Operators to Tree Pattern Queries on Graph-Structured Data.
  • Efficient Subgraph Matching on Billion Node Graphs.
  • Efficient Subgraph Similarity Search on Large Probabilistic Graph Databases.
  • Real Time Discovery of Dense Clusters in Highly Dynamic Graphs: Identifying Real World Events in Highly Dynamic Environments.
  • Injecting Uncertainty in Graphs for Identity Obfuscation.
  • Measuring Two-Event Structural Correlations on Graphs.
  • Understanding and Managing Cascades on Large Graphs.
  • Graph Synopses, Sketches, and Streams: A Survey.


2013
  • Large Scale Cohesive Subgraphs Discovery for Social Network Visual Analysis.
  • Piggybacking on Social Networks
  • Mobility and Social Networking: A Data Management Perspective
  • Top-K Structural Diversity Search in Large Networks
  • Diversified Top-k Graph Pattern Matching
  • Probabilistic Query Rewriting for Efficient and Effective Keyword Search on Graph Data
  • Summarizing Answer Graphs Induced by Keyword Queries
  • Scaling Queries over Big RDF Graphs with Semantic Hash Partitioning
  • Distributed SociaLite: A Datalog-Based Language for Large-Scale Graph Analysis
  • Horton+: A Distributed System for Processing Declarative Reachability Queries over Partitioned Graphs
  • Fast Iterative Graph Computation with Block Updates


2014

  • Toward a Distance Oracle for Billion-Node Graphs

    • From "Think Like a Vertex" to "Think Like a Graph"
    • GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph
    • Schemaless and Structureless Graph Querying
    • Optimizing Graph Algorithms on Pregel-like Systems
    • A Principled Approach to Bridging the Gap between Graph Data and their Schemas
    • Finding the Cost-Optimal Path with Time Constraint over Time-Dependent Graphs
    • Path Problems in Temporal Graphs
    • Computing Personalized PageRank Quickly by Exploiting Graph Structures
    • An Experimental Comparison of Pregel-like Graph Processing Systems
    • Distributed Graph Simulation: Impossibility and Possibility
    • Real-Time Twitter Recommendation: Online Motif Detection in Large Dynamic Graphs
    • Large-Scale Graph Analytics in Aster 6: Bringing Context to Big Data Discovery
    • Graph-based Data Integration and Business Intelligence with BIIIG
    • VERTEXICA: Your Relational Friend for Graph Analytics
    • NScale: Neighborhood-centric Analytics on Large Graphs
    • Systems for Big-Graphs
    • Pregel Algorithms for Graph Connectivity Problems with Performance Guarantees
    • Auto-Approximation of Graph Computing
    • LogGP: A Log-based Dynamic Graph Partitioning Method
    • Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs
    • GeoScope: Online Detection of Geo-Correlated Information Trends in Social Networks
    • An efficient reconciliation algorithm for social networks
    • Hop Doubling Label Indexing for Point-to-Point Distance Querying on Scale-Free Networks
    • Pronto: A Software-Defined Networking based System for Performance Management of Analytical Queries on Distributed Data Stores
    • Getting Your Big Data Priorities Straight: A Demonstration of Priority-based QoS using Social-network-driven Stock Recommendation
    • SPOT: Locating Social Media Users Based on Social Network Context
    • Matching Titles with Cross Title Web-Search Enrichment and Community Detection

    2015

  • Leveraging Graph Dimensions in Online Graph Search

    • Pregelix: Big(ger) Graph Analytics on a Dataflow Engine
    • Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation
    • MOCgraph: Scalable Distributed Graph Processing Using Message Online Computing
    • Event Pattern Matching over Graph Streams
    • Fast Failure Recovery in Distributed Graph Processing Systems
    • The More the Merrier: Efficient Multi-Source Graph Traversal
    • Exploiting Vertex Relationships in Speeding up Subgraph Isomorphism over Large Graphs
    • An Efficient Similarity Search Framework for SimRank over Large Dynamic Graphs
    • FrogWild! - Fast PageRank Approximations on Graph Engines
    • Giraph Unchained: Barrierless Asynchronous Parallel Execution in Pregel-like Graph Processing Systems
    • Scalable Subgraph Enumeration in MapReduce
    • Growing a Graph Matching from a Handful of Seeds
    • SCAN++: Efficient Algorithm for Finding Clusters, Hubs and Outliers on Large-scale Graphs
    • GraphMat: High performance graph analytics made productive
    • Taming Subgraph Isomorphism for RDF Query Processing
    • GraphTwist: Fast Iterative Graph Computation with Two-tier Optimizations
    • Bonding Vertex Sets Over Distributed Graph: A Betweenness Aware Approach
    • A Scalable Distributed Graph Partitioner
    • Association Rules with Graph Patterns
    • Performance and Scalability of Indexed Subgraph Query Processing Methods
    • Keys for Graphs
    • One Trillion Edges: Graph Processing at Facebook-Scale
    • Perseus: An Interactive Large-Scale Graph Mining and Visualization Tool
    • VIIQ: Auto-Suggestion Enabled Visual Interface for Interactive Graph Query Formulation
    • Query-Oriented Summarization of RDF Graphs
    • Universal-DB: Towards Representation Independent Graph Analytics
    • GraphGen: Exploring Interesting Graphs in Relational Data
    • On Uncertain Graphs Modeling and Queries
    • Towards Maximum Independent Sets on Massive Graphs
    • Tracking the Conductance of Rapidly Evolving Topic-Subgraphs
    • Influential Community Search in Large Networks
    • Efficient Partial-Pairs SimRank Search for Large Networks
    • Walk, Not Wait: Faster Sampling Over Online Social Networks
    • Community Detection in Social Networks: An In-depth Benchmarking Study with a Procedure-Oriented Framework
    • Leveraging History for Faster Sampling of Online Social Networks







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    Top Conferences



    2012-2015
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    Graph @ Middleware


    2015
    2014 


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    2016年2月17日水曜日

    Target Conferences



    April 3rd 

    Supercomputing 2016  -- Graph500 Paper


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    2016年1月23日土曜日

    Apache Flink

    http://www.infoworld.com/article/2919602/hadoop/flink-hadoops-new-contender-for-mapreduce-spark.html
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    2014年12月13日土曜日

    Visualization

    Fisheye
    http://bost.ocks.org/mike/fisheye/

    D3.js (Data Driven Document)
    http://d3js.org/
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    ラベル:

    2014年12月5日金曜日

    Elastic ScaleGraph

    Elastic Graph Processing Library on X10 2.5 (Elastic X10) 
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    ラベル:

    2014年12月3日水曜日

    Deep Learning

    Application to Natural Language Processing : http://www.socher.org/

    Distributed Neural Networks with GPUs in the AWS Cloud
    http://techblog.netflix.com/2014/02/distributed-neural-networks-with-gpus.html

    Large Scale Distributed Deep Networks
    http://static.googleusercontent.com/media/research.google.com/en//archive/large_deep_networks_nips2012.pdf


    Open Software

    Deep Learning for Java
    http://deeplearning4j.org/

    Deeplearning4j is the first commercial-grade, open-source deep-learning library written in Java. It is meant to be used in business environments, rather than as a research tool for extensive data exploration. Deeplearning4j is most helpful in solving distinct problems, like identifying faces, voices, spam or e-commerce fraud. Deeplearning4j integrates with GPUs and includes a versatile n-dimensional array class. DL4J aims to be cutting-edge plug and play, more convention than configuration. By following its conventions, you get an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. This Java deep-learning library has a domain-specific language for neural networks that serves to turn their multiple knobs. Deeplearning4j includes a distributed deep-learning framework and a normal deep-learning framework (i.e. it runs on a single thread as well). Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure, since they’re written for the JVM. This open-source, distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models. By following the links at the bottom of each page, you will learn to set up, and train with sample data, several types of deep-learning networks. These include single- and multithread networks, Restricted Boltzmann machines, deep-belief networks, Deep Autoencoders, Recursive Neural Tensor Networks, Convolutional Nets and Stacked Denoising Autoencoders.
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    2014年1月16日木曜日

    渡部君・金刺君とのミーティングメモ

    DEIM 2014の論文：Dropbox/SuzumuraLab/Projects/Social Analytics/watanabe/paper/DEIM2014/submit/draft_20140114.pdf 

    引き継ぎ、研究の方向性
    XAXIS（５章）の説明

    頂点を追加、削除→オブジェクトを消さず、インアクティブにする
    実行時のコスト（ガベージコレクションなど）は小さいが、メモリやノードの消費量が大きくなる
    シミュレーションの基盤を成長させていく（成長シミュレーション）
    今のプラットフォームのあるべき姿→研究テーマにする
    解析：最初のシミュレーション：インアクティブの割合が大きいと効果が大きい
    本来は1/10のノードでOK（6〜7千万／4億）

    ハードウェア
    ノードもダイナミックに追加する（X10ではできない？）
    Amazon EC2 のインスタンスを確保して、ストリーム処理する（石井先輩の研究）
    VMを必要に応じて予測、確保

    ソフトウェア：
    インスタンスの必要な量を予測して追加
    X10の他に基盤が必要（Hadoopは難しいが、できなくはない→YARNを使用する）

    グラフの処理系の上でgiraph上に、ノードを追加、Hadoopを実装
    VMやYARNの起動、頂点の追加削除による実行速度が落ちないように、
    成長モデルに応じた予測のアルゴリズムモデルを考える
    バルク確保によるメモリ消費量とのトレードオフも考慮する
    アダプティブなシミュレーション基盤を作る

    オーバーヘッドを考えるとXAXISの方が速いと思われるが、渡部先輩の実験結果（参考値）と比較する
    SocialMediaのデータ解析は、シミュレーションのモデルを作る上で重要
    スキルトランスファーをしてもらう
    確実な方法があるのか、試行錯誤する部分もあるのか

    まずは、HadoopをTSUBAMEで動かして、使い方を学ぶ
    解析をしたい時にRAがオーバーヘッドなく扱えるよう、ドキュメントを作っておく
    何を調べたいか、やりたいことは色々あるが、データが大きいため扱いにくい

    一部のデータ（属性情報）だけ取ってきて、それだけでフォローのモデルを作成する
    データ同化にもつながる（シミュレーションしながらモデルを改良していく）
    新規のユーザ追加のコストを削減
    いきなり数十ノードを最初から確保するのは現実的でない
    まずは小規模で実行し、ユーザ数が増えてきたら結果をダンプする
    規模が大きくなったら途中結果を読み込んで、やり直す
    スナップショットを少しずつ取りながら、ノードを確保していく
    X10は動的なアロケーションができない
    他の新規ノードが追加できるような基盤を作る

    twitterの追加解析も行う
    クラスタリング係数値、Connected Components
    ScaleGraphのライブラリも使っていく
    Webマイニングの話にもかかわる
    既存研究のWebマイニングの論文を調べて、どうネットワーク解析をしていくか
    デフォルトの（通常行われている）解析セットを調査する
    そこから発展させて、分かっていないことを仮説を立てて検証する
    「何次の隔たり」よりも、もう少しミクロ（詳細）な仮説の方が良い
    tweet そのものの解析も行う
    投稿した内容、頻度から、ユーザのプロパティを予想する
    データ取得（クロール）の方法も教えてもらう

    “What is twitter”で行われていないことは？
    retweetの木構造も研究されていた
    リアルタイムに変わるGraphは、PageRankを計算すると異なる
    グラフの構造の変化はリアルタイムにある
    グラフ分割、BC、PageRank、クラスタリングをグラフストリーム的に計算する
    tweetも、テキスト処理をしてクラスタリング（類似度計算）できる
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    2014年1月10日金曜日

    ACM SIGSIM PADS 2013

    Here is a list of accepted papers for PADS 2013 that are all relevant to our agent-based simulations.
    http://www.acm-sigsim-pads.org/acceptedPapers.htm

    • Accelerating Optimistic HLA-based Simulations in Virtual Execution Environments
    • A Time Management Optimization Framework for Large-Scale Distributed Hardware-In-The-Loop Simulation
    • An Expansion-aided Synchronous Conservative Time Management Algorithm on GPU
    • Towards Performance Evaluation of Conservative Distributed Discrete-Event Network Simulations Using Second-Order Simulation
    • Event Pool Structures for PDES on Many-Core Beowulf Clusters
    • Modeling Communication Software Execution for Accurate Simulation of Distributed Systems
    • GPU Accelerated Three-stage Execution Model for Event-Parallel Simulation
    • Designing Computational Steering Facilities for Distributed Agent Based Simulations
    • TerraME HPA: Parallel Simulation of Multi-Agent Systems over SMPs
    • Optimizing Parallel Simulation of Multicore Systems Using Domain-Specific Knowledge
    • Data Assimilation in Agent Based Simulation of Smart Environment
    • On the Parallel Simulation of Scale-Free Networks
    • Warp Speed: Executing Time Warp on 1,966,080 Cores

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    GraphLab's Blog

    A co-founder of a fast-growing startup company named "Graphlab" - launched from Carnegie Mellon University - has just blogged about our ScaleGraph project. That's a great news for celebrating a new year 2014, and we definitely accelerate the speed to become the leading project in this area. 
    http://bickson.blogspot.ie/
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    SIGSIM PADS 2014 CFP

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    2014年1月7日火曜日

    GRADES 2013

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    2014年1月2日木曜日

    Distributed-Memory Parallel Algorithms for Generating Massive Scale-free Networks Using Preferential Attachment Model

    Distributed-Memory Parallel Algorithms for Generating Massive Scale-free Networks Using Preferential Attachment Model 
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    2013年12月27日金曜日

    TurboGraph

     TurboGraph: a fast parallel graph engine handling billion-scale graphs in a single PC
    http://dl.acm.org/citation.cfm?id=2487581

    Graphs are used to model many real objects such as social networks and web graphs. Many real applications in various fields require efficient and effective management of large-scale graph structured data. Although distributed graph engines such as GBase and Pregel handle billion-scale graphs, the user needs to be skilled at managing and tuning a distributed system in a cluster, which is a nontrivial job for the ordinary user. Furthermore, these distributed systems need many machines in a cluster in order to provide reasonable performance. In order to address this problem, a disk-based parallel graph engine called Graph-Chi, has been recently proposed. Although Graph-Chi significantly outperforms all representative (disk-based) distributed graph engines, we observe that Graph-Chi still has serious performance problems for many important types of graph queries due to 1) limited parallelism and 2) separate steps for I/O processing and CPU processing. In this paper, we propose a general, disk-based graph engine called TurboGraph to process billion-scale graphs very efficiently by using modern hardware on a single PC. TurboGraph is the first truly parallel graph engine that exploits 1) full parallelism including multi-core parallelism and FlashSSD IO parallelism and 2) full overlap of CPU processing and I/O processing as much as possible. Specifically, we propose a novel parallel execution model, calledpin-and-slide. TurboGraph also provides engine-level operators such as BFS which are implemented under the pin-and-slide model. Extensive experimental results with large real datasets show that TurboGraph consistently and significantly outperforms Graph-Chi by up to four orders of magnitude! Our implementation of TurboGraph is available at ``http://wshan.net/turbograph}" as executable files.
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    2013年12月2日月曜日

    System Software Papers in ICWS 2012

    Here is a list of papers on system softwares in ICWS 2012. A keyword "service" is widely used in this conference, so important thing is how our efforts could be abstracted out towards the combination with "Service".

    • Highly Resilient Systems for Cloud
    • Parallel Computing Framework as a Cloud Service
    • Overcoming Large Data Transfer Bottlenecks in RESTful Service Orchestrations, ICWS 2012
    • RESTful Web Service Mashup Based Coal Mine Safety Monitoring and Control Automation with Wireless Sensor Network, , ICWS 2012
    • Intelligent Database Placement in Cloud Environment, , ICWS 2012
    • Andes: A Highly Scalable Persistent Messaging System , ICWS 2012
    • Enabling Advanced Loading Strategies for Data Intensive Web Services, ICWS 2012
    • Green Web Services: Modeling and Estimating Power Consumption of Web Services, ICWS 2012
    • A Network Coordinate Based Web Service Positioning Framework for Response Time Prediction, ICWS 2012
    • Disk-Offload Middleware for Web-Services Using the Application-Caching Paradigm, ICWS 2012
    • Scaling Spatial Alarm Services on Road Networks, ICWS 2012

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    2013年12月1日日曜日

    ICWS 2014

    I have been invited as a program committee member for ICWS 2014. Over the past several years, I was involved with some projects related to service oriented computing by focusing more on system software level optimization methods such as differential parsing/deserialization method for high performance XML processing. By just reading through a series of titles in ICWS 2013, most of the researches do not really sound attractive in a practical setting in real world. They still find out a way on how to formulate composite web services in an automatic manner, which is a long standing problem and never solved.  That's not really what we should pursue in this research area.

    However we can not really ignore this conference since it is treated as a top-tier conference. So what do we do in this context ? We have been pursing on big data / graph processing, stream computing, large-scale agent simulation technology, and so forth. Sometimes you can find a good research topic by looking at the boarder space between two different research areas. In that way, we can start new research topic. So what are they ? 
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    2013年7月12日金曜日

    Graph analytics in bioinformatics

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    2013年7月9日火曜日

    道路ネットワーク縮退化による高速化

    金刺君の実験により、Open Street Map の冗長な道路ネットワークから、必要のない交差点を統合すると、かなり高速化できることが確認できた。現状はナイーブな方法（一本道は統合）だが、精度を加味して、動的に統合することも面白いだろう。単に統合するのではなく、仮想的な道路を作るのでも良い。また統合することにより、車両の位置（緯度、経度）が不正確になるが、これはマッピング情報を作るべき。


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    2013年6月6日木曜日

    Facebook's TAO

    TAO: Facebook’s Distributed Data Store for the Social Graph
    We introduce a simple data model and API tailored for serving the social graph, and TAO, an implementation of this model. TAO is a geographically distributed data store that provides efficient and timely access to the social graph for Facebook’s demanding workload using a fixed set of queries. It is deployed at Facebook, replacing memcache for many data types that fit its model. The system runs on thousands of machines, is widely distributed, and provides access to many petabytes of data. TAO can process a billion reads and millions of writes each second
    https://www.usenix.org/conference/atc13/tao-facebook%E2%80%99s-distributed-data-store-social-graph
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    2013年5月30日木曜日

    Graph Applications in Cybersecurity Domain

    Large Scale Graph Analytics and Randomized Algorithms for Applications
    in Cybersecurity
    http://dl.acm.org/citation.cfm?id=2459984
    http://www.graphanalysis.org/SIAM-CSE13/02_Johnson.pdf

    A network hacking attack in which hackers repeatedly steal password hashes and move through a computer network with the goal of reaching a computer with high level administrative privileges is known as a pass-the-hash attack. In this paper we apply graph coarsening on graphs obtained from computer network data for the purpose of (a) detecting hackers using this attack and (b) assessing the risk level of the network's current state. We repeatedly contract edges (obtaining a graph minor), which preserves the existence of paths in the graph, and take powers of the adjacency matrix to count the paths. This allows us to detect the existence of paths as well as find paths that have high risk of being exploited by adversaries.
    W. Eberle and L. Holder. Applying graph-based anomaly detection approaches to the discovery of insider threats. In IEEE International Conference on Intelligence and Security Informatics (ISI), 2009.

    D. A. Spielman and N. Srivastava. Graph sparsification by effective resistances. In Proc. 40th Annual ACM Symposium on Theory of Computing, 2008.
    S. Jajodia, S. Noel, and B. O'Berry. Topological analysis of network attack vulnerability. In V. Kumar, J. Srivastava, and A. Lazarevic, editors, Managing Cyber Threats: Issues, Approaches and Challenges, pages 248--266. Kluwer Academic Publisher, 2005.
    http://vulcan.ee.iastate.edu/~gmani/personal/papers/journals/IEEE-PS-08.pdf

     Measuring Security Risk of Networks Using Attack Graphs
    http://users.encs.concordia.ca/~wang/papers/ijngc10.pdf

     U Kang's Research Goal Statement
    http://www.cs.cmu.edu/~ukang/ukang-research.pdf

     http://www.eecs.wsu.edu/~holder/pubs/EberleCATCH09.pdf
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    2013年5月22日水曜日

    Cassovary: Twitter's Graph Processing Library

    http://engineering.twitter.com/2012/03/cassovary-big-graph-processing-library.html
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    2013年4月21日日曜日

    Open Data Initiative

    Open Data Initiative
    http://www.theodi.org/


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    Sociable Physics

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    2013年3月29日金曜日

    Open Data

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    2012年11月9日金曜日

    Location Prediction from Tweets

    http://infolab.cse.tamu.edu/static/papers/cikm1184c-cheng.pdf
    http://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdf
    http://doras.dcu.ie/16754/1/nohare_paper.pdf
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    2012年11月6日火曜日

    Interactive Hybrid Simulation of Large Traffic

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    2012年9月17日月曜日

    Spectral Analysis for Billion-Scale Graphs: Discoveries and Implementation

    Spectral Analysis for Billion-Scale Graphs: Discoveries and Implementation
    http://www.cs.cmu.edu/~ukang/papers/HeigenPAKDD2011.pdf

    Ogata-kun's work should refer the above effort ...
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