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Highlighted notes while research with Prof. Dip Sankar Banerjee, Prof. Kishore Kothapalli: PageRank on an evolving graph - Yanzhao Yang. https://theory.utdallas.edu/seminar/G2S13/YY/Pagerank%20on%20evolving%20graph-Yanzhao%20Yang.pdf Pagerank may be always imprecise, due to lack of knowledge of up-to-date/complete graph. Millions of hyperlinks/social-links modified each day. Which portions of the web should a crawler focus most (probing strategy)? Probing techniques discussed are Random probing, Round-robin probing, Proportional probing (random, proportional to node's pagerank), Priority probing (deterministic, pick node with highest cumulative pagerank sum), Hybrid probing (proportional + round-robin).

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mBot 教學2 mBlock積木式設計程式

mBot 教學2 mBlock積木式設計程式

コンセプトから理解するGitコマンド

コンセプトから理解するGitコマンド

第2部 自作ライブラリ紹介

第2部 自作ライブラリ紹介

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mBot 教學2 mBlock積木式設計程式

Updated on October 8, 2017
1.基本程式邏輯
2.mBlock IDE
3.mBlock指令積木
4.mBlock應用實例
5.通訊擴充指令

コンセプトから理解するGitコマンド

会社関係の勉強会向けに作った資料です。
パラパラマンガ調のためページ数は多いですが、内容は基礎的なものです。
このスライドを読み終わった人にオススメ： 「図解gitworkflows(7)」
資料一覧: https://docs.google.com/spreadsheets/d/1VZMz_31Z7FQBnK139o8yMqzwrTJgZWtPqgoG-mx1zh0/edit?usp=sharing

第2部 自作ライブラリ紹介

自作FBXパーサの紹介。
東工大ロ技研のrogyゼミ2016/01での発表資料その2。
その1はこちら: http://www.slideshare.net/L1048576/fbx-1-1

MAYAで作ったアニメーションをUnityに取り込んで動かしてみるの巻

2017年おおさかクリエイティブミーティングVol.1でお話したスライドになります。

GLSLtech2018 レイマーチングで半歩差のつく小技集

9/13 登壇したプレゼンテーション資料です

【CEDEC2016】横スクロールARPG 「追憶の青」における 2Dキャラクターアニメーション〜2Dアニメの注意点とテクニック〜

CEDEC2016で講演した内容のスライドです。
※アニメーションが含まれますので、スライドをダウンロードしてご覧ください。

Salesforce と kintone 徹底比較

kitnone cafe 埼玉 vol1資料

PFI会社案内

株式会社Preferred Infrastructureの会社紹介と製品紹介です。

Clean Architecture for Unity

The document is a copyright notice repeated over 65 lines. It contains no other substantive information.

SVGでつくるインタラクティブWebアプリケーション

2012年10月6日に開催された神戸ITフェスティバル 2012におけるセミナー枠で使用したプレゼン資料です。
サンプルコードはこちら↓
https://github.com/kadoppe/kobe-it-fes-2012

「スキルなし・実績なし」 32歳窓際エンジニアがシリコンバレーで働くようになるまで

「全然使えないおっさんが入ってきた」状態のつらい状況から這い上がるきっかけとなった3つのターニングポイントについての話。
@TechCrunch Tokyo ハッカソン Tech Talk
関連記事：『人生初の講演をしました』
http://d.hatena.ne.jp/shu223/20131111/1384156668
もしよろしければ。。
http://www.amazon.co.jp/registry/wishlist/3OXBFWIH88643

日本のメイカー活動とNT金沢

in メイカーズながおか2020

Encoder-decoder 翻訳 (TISハンズオン資料)

TIS株式会社のハンズオンで使用した講義資料です。

実践QBVH

2013/8/24 レイトレ合宿用に作成したQBVHの解説スライドです。

mBot教學(3) - 開發mBot應用程式

擴充mBot指令、mBot指令簡介、連線測試mBot程式、離線測試mBot程式、使用M部落App學習mBot程式、使用M部落App設計mBot程式

『禍つヴァールハイト』モバイルにおけるプレイヤー最大100体同時表示可能なグラフィックス最適化について

CEDEC2019
モバイルにおけるハイエンド向けタイトルの設計・最適化手法。

こわくない Git

「マージがなんとなく怖い」「リベースするなって怒られて怖い」「エラーが出て怖い」
Git 入門者にありがちな「Git 怖い」を解消するため、Git のお仕事（コミット、ブランチ、マージ、リベース）について解説します。

DDD Alliance レガシーなコードにドメイン駆動設計で立ち向かった5年間の軌跡

30年間、事業を支えてきた業務システムをDDDで刷新する。
そのためには、組織的、エンジニアのレベルなど多くの問題があります。
その壁をどう乗り越えたのか？ そして、壁の向こうで得た恩恵とは何のか？
5年という期間を経て、得ることのできた気づきや組織的な変化をお伝えしたいです。

15分でわかるモバイルアクセシビリティ

2016年8月に株式会社サイバーエージェントで開催された社内勉強会で使用したスライドです。

mBot 教學2 mBlock積木式設計程式

mBot 教學2 mBlock積木式設計程式

コンセプトから理解するGitコマンド

コンセプトから理解するGitコマンド

第2部 自作ライブラリ紹介

第2部 自作ライブラリ紹介

MAYAで作ったアニメーションをUnityに取り込んで動かしてみるの巻

MAYAで作ったアニメーションをUnityに取り込んで動かしてみるの巻

GLSLtech2018 レイマーチングで半歩差のつく小技集

GLSLtech2018 レイマーチングで半歩差のつく小技集

【CEDEC2016】横スクロールARPG 「追憶の青」における 2Dキャラクターアニメーション〜2Dアニメの注意点とテクニック〜

【CEDEC2016】横スクロールARPG 「追憶の青」における 2Dキャラクターアニメーション〜2Dアニメの注意点とテクニック〜

Salesforce と kintone 徹底比較

Salesforce と kintone 徹底比較

PFI会社案内

PFI会社案内

Clean Architecture for Unity

Clean Architecture for Unity

SVGでつくるインタラクティブWebアプリケーション

SVGでつくるインタラクティブWebアプリケーション

「スキルなし・実績なし」 32歳窓際エンジニアがシリコンバレーで働くようになるまで

「スキルなし・実績なし」 32歳窓際エンジニアがシリコンバレーで働くようになるまで

Windowsの画面スケーリングをきちんと理解しよう

Windowsの画面スケーリングをきちんと理解しよう

日本のメイカー活動とNT金沢

日本のメイカー活動とNT金沢

Encoder-decoder 翻訳 (TISハンズオン資料)

Encoder-decoder 翻訳 (TISハンズオン資料)

実践QBVH

実践QBVH

mBot教學(3) - 開發mBot應用程式

mBot教學(3) - 開發mBot應用程式

『禍つヴァールハイト』モバイルにおけるプレイヤー最大100体同時表示可能なグラフィックス最適化について

『禍つヴァールハイト』モバイルにおけるプレイヤー最大100体同時表示可能なグラフィックス最適化について

こわくない Git

こわくない Git

DDD Alliance レガシーなコードにドメイン駆動設計で立ち向かった5年間の軌跡

DDD Alliance レガシーなコードにドメイン駆動設計で立ち向かった5年間の軌跡

15分でわかるモバイルアクセシビリティ

15分でわかるモバイルアクセシビリティ

쉽게 설명하는 GAN (What is this? Gum? It's GAN.)

The document discusses generative adversarial networks (GANs). It begins with an introduction to GANs, describing their concept and training process. It then reviews a seminal GAN paper, discussing its mathematical formulation of GAN training as a minimax game and theoretical results showing global optimality can be achieved. The document concludes by outlining the configuration, implementation, and flowchart for a GAN experiment.

# Can we trust ai. the dilemma of model adjustment

This document provides a summary of an AI expert's background and experience, and then discusses some challenges in ensuring the trustworthiness of AI models. It notes that while models may perform well during training, their performance can decline when deployed in the real world due to new data, noise, and errors. Interpretable modeling techniques like LIME and Grad-CAM are introduced to help evaluate whether models' predictions are appropriate and diagnose issues. The discussion emphasizes that identification of errors is not enough, and ways to correct models must also be explored, such as improving data quality.

Distributed Keyword Search over RDF via MapReduce

Non expert users need support to access linked data available on the Web. To this aim, keyword-based search is considered an essential feature of database systems.
The distributed nature of the Semantic Web demands query processing techniques to evolve towards a scenario where data is scattered on distributed data stores. Existing approaches to keyword search cannot guarantee scalability in a distributed environment, because, at runtime, they are unaware of the location of the relevant data to the query and thus, they cannot optimize join tasks.
In this paper, we illustrate a novel distributed approach to keyword search over RDF data that exploits the MapReduce paradigm by switching the problem from graph-parallel to data-parallel processing. Moreover, our framework is able to consider ranking during the building phase to return directly the best (top-k) answers in the first (k) generated results, reducing greatly the overall computational load and complexity.
Finally, a comprehensive evaluation demonstrates that our approach exhibits very good efficiency guaranteeing high level of accuracy, especially with respect to state-of-the-art competitors.

Incremental Page Rank Computation on Evolving Graphs : NOTES

Highlighted notes while doing research work under Prof. Dip Sankar Banerjee and Prof. Kishore Kothapalli:
Incremental Page Rank Computation on Evolving Graphs.
https://dl.acm.org/doi/10.1145/1062745.1062885
This paper describes a simple method for computing dynamic pagerank, based on the fact that change of out-degree of a node does not affect its pagerank (first order markov property). The part of graph which is updated (edge additions / edge deletions / weight changes) is used to find the affected partition of graph using BFS. The unaffected partition is simply scaled, and pagerank computation is done only for the affected partition.

Rsgan iconi

C. Kim, S. Jung, E. Hwang. "RSGAN: Regularization-on-Sigma GAN". KSII The 10th International Conference on Internet (ICONI) 2018., PhnomPenh, Cambodia, 2018.12
page 6 is a video. So you can't watch it.

[系列活動] Data exploration with modern R

This document provides an introduction to exploring and visualizing data using the R programming language. It discusses the history and development of R, introduces key R packages like tidyverse and ggplot2 for data analysis and visualization, and provides examples of reading data, examining data structures, and creating basic plots and histograms. It also demonstrates more advanced ggplot2 concepts like faceting, mapping variables to aesthetics, using different geoms, and combining multiple geoms in a single plot.

zanardi

The document describes an adiabatic quantum algorithm for computing PageRank vectors. PageRank is the principal eigenvector of the Google matrix G, which represents the probability of a random web surfer moving between pages. The algorithm maps the PageRank computation to the ground state of a Hamiltonian whose ground state encodes the PageRank vector. It was found that this algorithm could prepare the PageRank vector in time scaling as poly(log n), providing an exponential speedup over classical algorithms. Additionally, the top ranked pages in the PageRank vector could be read out with a polynomial speedup over classical methods.

R visualization: ggplot2, googlevis, plotly, igraph Overview

In this workshop you will learn about 4 R packages to perform data visualization: ggplot2, googlevis, plotly and igraph. You will learn about their strengths and weaknesses. Code snippets are provides.

SEMAC Graph Node Embeddings for Link Prediction

We present a new graph representation learning approach called SEMAC that jointly exploits fine-grained node features as well as the overall graph topology. In contrast to the SGNS or SVD methods espoused in previous representation-based studies, our model represents nodes in terms of subgraph embeddings acquired via a form of convex matrix completion to iteratively reduce the rank, and thereby, more effectively eliminate noise in the representation. Thus, subgraph embeddings and convex matrix completion are elegantly integrated into a novel link prediction framework.

Extended Property Graphs and Cypher on Gradoop

Presented at the First openCypher Implementers Meeting in Walldorf, Germany, February 2017 @ http://www.opencypher.org/blog/2017/03/31/first-ocim-blog/

Optimization_methods.pdf

It describes the various unconstrained optimization methods, that includes first order as well as second order methods.

Streaming Python on Hadoop

Data Science is concerned with the analysis of large amounts of data. When the volume of data is really large, it requires the use of cooperating, distributed machines. The most popular method of doing this is Hadoop, a collection of programs to perform computations on connected machines in a cluster. Hadoop began life as an open-source implementation of MapReduce, an idea first developed and implemented by Google for its own clusters. Though Hadoop's MapReduce is Java-based, and quite complex, this talk focuses on the "streaming" facility, which allows Python programmers to use MapReduce in a clean and simple way. We will present the core ideas of MapReduce and show you how to implement a MapReduce computation using Python streaming. The presentation will also include an overview of the various components of the Hadoop "ecosystem."
NYC Data Science Academy is excited to welcome Sam Kamin who will be presenting an Introduction to Hadoop for Python Programmers a well as a discussion of MapReduce with Streaming Python.
Sam Kamin was a professor in the University of Illinois Computer Science Department. His research was in programming languages, high-performance computing, and educational technology. He taught a wide variety of courses, and served as the Director of Undergraduate Programs. He retired as Emeritus Associate Professor, and worked at Google until taking his current position as VP of Data Engineering in NYC Data Science Academy.
--------------------------------------
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.

Using Graph Algorithms for Advanced Analytics - Part 2 Centrality

What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.

Graph Gurus Episode 27: Using Graph Algorithms for Advanced Analytics Part 2

Full Webinar: https://info.tigergraph.com/graph-gurus-27
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms. Join us for Part 2 of our five-part webinar series on using graph algorithms for advanced analytics.
By attending this webinar you will:
- Hear about use cases for centrality graph algorithms
- Learn how to select the right algorithm for your use case
- Be able to run and tailor GSQL graph algorithms

Local Approximation of PageRank

Implementation of Local Approximation of PageRank and comparison with actual PageRank (DataSet-Wikipedia)
https://github.com/sjuyal/MajorIRE

Recommendation and graph algorithms in Hadoop and SQL

A talk I gave at ancestry.com on Hadoop, SQL, recommendation and graph algorithms. It's a tutorial overview, there are better algorithms than those I describe, but these are a simple starting point.

Tutorial "Linked Data Query Processing" Part 2 "Theoretical Foundations" (WWW...

This document summarizes the theoretical foundations of linked data query processing presented in a tutorial. It discusses the SPARQL query language, data models for linked data queries, full-web and reachability-based query semantics. Under full-web semantics, a query is computable if its pattern is monotonic, and eventually computable otherwise. Reachability-based semantics restrict queries to data reachable from a set of seed URIs. Queries under this semantics are always finitely computable if the web is finite. The document outlines computability results and properties regarding satisfiability and monotonicity for different semantics.

Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1

Full Webinar: https://info.tigergraph.com/graph-gurus-26
Have you ever wondered how routing apps like Google Maps find the best route from one place to another? Finding that route is solved by the Shortest Path graph algorithm. Today, graph algorithms are moving from the classroom to a host of important and valuable operational and analytical applications. This webinar will give you an overview of graph algorithms, how to use them, and the categories of problems they can solve, and then take a closer look at path algorithms. This webinar is the first part in a five-part series, each part examining a different type of problem to be solved.

Dagstuhl seminar talk on querying big graphs

The document describes techniques for querying large graph datasets. It discusses how graphs arise in many domains like social networks, biological networks, and knowledge graphs. It outlines some challenges in querying big graphs like heterogeneity, uncertainty, and massive scale. It then presents the author's work on approximate subgraph matching to enable flexible querying of heterogeneous graphs. The key ideas are converting graph structures to vectors to compute similarity, defining a cost function for subgraph matching, and using loopy belief propagation for inference. Experimental results on real datasets demonstrate the effectiveness of the proposed techniques.

Prediction of route and destination intent shibumon alampatta

This document discusses predicting a driver's intended route and destination using a Hidden Markov Model approach. It begins by outlining the problem and applications, such as route guidance and fuel efficiency. It then discusses using a probabilistic model to capture the sequential and routine nature of driving behavior. The model represents routes as a graph and states as links and goals. It is trained on past trip data to learn transition probabilities and predict the next link and destination. The approach achieves over 80% accuracy on average but has limitations for non-routine trips. Possible enhancements include additional parameters and expanding to other domains.

쉽게 설명하는 GAN (What is this? Gum? It's GAN.)

쉽게 설명하는 GAN (What is this? Gum? It's GAN.)

# Can we trust ai. the dilemma of model adjustment

# Can we trust ai. the dilemma of model adjustment

Distributed Keyword Search over RDF via MapReduce

Distributed Keyword Search over RDF via MapReduce

Incremental Page Rank Computation on Evolving Graphs : NOTES

Incremental Page Rank Computation on Evolving Graphs : NOTES

Rsgan iconi

Rsgan iconi

[系列活動] Data exploration with modern R

[系列活動] Data exploration with modern R

zanardi

zanardi

R visualization: ggplot2, googlevis, plotly, igraph Overview

R visualization: ggplot2, googlevis, plotly, igraph Overview

SEMAC Graph Node Embeddings for Link Prediction

SEMAC Graph Node Embeddings for Link Prediction

Extended Property Graphs and Cypher on Gradoop

Extended Property Graphs and Cypher on Gradoop

Optimization_methods.pdf

Optimization_methods.pdf

Streaming Python on Hadoop

Streaming Python on Hadoop

Using Graph Algorithms for Advanced Analytics - Part 2 Centrality

Using Graph Algorithms for Advanced Analytics - Part 2 Centrality

Graph Gurus Episode 27: Using Graph Algorithms for Advanced Analytics Part 2

Graph Gurus Episode 27: Using Graph Algorithms for Advanced Analytics Part 2

Local Approximation of PageRank

Local Approximation of PageRank

Recommendation and graph algorithms in Hadoop and SQL

Recommendation and graph algorithms in Hadoop and SQL

Tutorial "Linked Data Query Processing" Part 2 "Theoretical Foundations" (WWW...

Tutorial "Linked Data Query Processing" Part 2 "Theoretical Foundations" (WWW...

Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1

Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1

Dagstuhl seminar talk on querying big graphs

Dagstuhl seminar talk on querying big graphs

Prediction of route and destination intent shibumon alampatta

Prediction of route and destination intent shibumon alampatta

About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...

TrueTime is a service that enables the use of globally synchronized clocks, with bounded error. It returns a time interval that is guaranteed to contain the clock’s actual time for some time during the call’s execution. If two intervals do not overlap, then we know calls were definitely ordered in real time. In general, synchronized clocks can be used to avoid communication in a distributed system.
The underlying source of time is a combination of GPS receivers and atomic clocks. As there are “time masters” in every datacenter (redundantly), it is likely that both sides of a partition would continue to enjoy accurate time. Individual nodes however need network connectivity to the masters, and without it their clocks will drift. Thus, during a partition their intervals slowly grow wider over time, based on bounds on the rate of local clock drift. Operations depending on TrueTime, such as Paxos leader election or transaction commits, thus have to wait a little longer, but the operation still completes (assuming the 2PC and quorum communication are working).

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.

Adjusting Bitset for graph : SHORT REPORT / NOTES

Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is commonly used for efficient graph computations. Unfortunately, using CSR for dynamic graphs is impractical since addition/deletion of a single edge can require on average (N+M)/2 memory accesses, in order to update source-offsets and destination-indices. A common approach is therefore to store edge-lists/destination-indices as an array of arrays, where each edge-list is an array belonging to a vertex. While this is good enough for small graphs, it quickly becomes a bottleneck for large graphs. What causes this bottleneck depends on whether the edge-lists are sorted or unsorted. If they are sorted, checking for an edge requires about log(E) memory accesses, but adding an edge on average requires E/2 accesses, where E is the number of edges of a given vertex. Note that both addition and deletion of edges in a dynamic graph require checking for an existing edge, before adding or deleting it. If edge lists are unsorted, checking for an edge requires around E/2 memory accesses, but adding an edge requires only 1 memory access.

Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.

Adjusting primitives for graph : SHORT REPORT / NOTES

Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).

Experiments with Primitive operations : SHORT REPORT / NOTES

This includes:
- Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
- Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
- Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
- Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).

PageRank Experiments : SHORT REPORT / NOTES

This includes:
- Adjusting data types for rank vector
- Adjusting Pagerank parameters
- Adjusting Sequential approach
- Adjusting OpenMP approach
- Comparing sequential approach
- Adjusting Monolithic (Sequential) optimizations (from STICD)
- Adjusting Levelwise (STICD) approach
- Comparing Levelwise (STICD) approach
- Adjusting ranks for dynamic graphs
- Adjusting Levelwise (STICD) dynamic approach
- Comparing dynamic approach with static
- Adjusting Monolithic CUDA approach
- Adjusting Monolithic CUDA optimizations (from STICD)
- Adjusting Levelwise (STICD) CUDA approach
- Comparing Levelwise (STICD) CUDA approach
- Comparing dynamic CUDA approach with static
- Comparing dynamic optimized CUDA approach with static

Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...

Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).

word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...

Below are the important points I note from the 2020 paper by Martin Grohe:
- 1-WL distinguishes almost all graphs, in a probabilistic sense
- Classical WL is two dimensional Weisfeiler-Leman
- DeepWL is an unlimited version of WL graph that runs in polynomial time.
- Knowledge graphs are essentially graphs with vertex/edge attributes
ABSTRACT:
Vector representations of graphs and relational structures, whether handcrafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view.
Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.

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.

Shared memory Parallelism (NOTES)

My notes on shared memory parallelism.
Shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs. Using memory for communication inside a single program, e.g. among its multiple threads, is also referred to as shared memory [REF].

A Dynamic Algorithm for Local Community Detection in Graphs : NOTES

**Community detection methods** can be *global* or *local*. **Global community detection methods** divide the entire graph into groups. Existing global algorithms include:
- Random walk methods
- Spectral partitioning
- Label propagation
- Greedy agglomerative and divisive algorithms
- Clique percolation
https://gist.github.com/wolfram77/b4316609265b5b9f88027bbc491f80b6
There is a growing body of work in *detecting overlapping communities*. **Seed set expansion** is a **local community detection method** where a relevant *seed vertices* of interest are picked and *expanded to form communities* surrounding them. The quality of each community is measured using a *fitness function*.
**Modularity** is a *fitness function* which compares the number of intra-community edges to the expected number in a random-null model. **Conductance** is another popular fitness score that measures the community cut or inter-community edges. Many *overlapping community detection* methods **use a modified ratio** of intra-community edges to all edges with atleast one endpoint in the community.
Andersen et al. use a **Spectral PageRank-Nibble method** which minimizes conductance and is formed by adding vertices in order of decreasing PageRank values. Andersen and Lang develop a **random walk approach** in which some vertices in the seed set may not be placed in the final community. Clauset gives a **greedy method** that *starts from a single vertex* and then iteratively adds neighboring vertices *maximizing the local modularity score*. Riedy et al. **expand multiple vertices** via maximizing modularity.
Several algorithms for **detecting global, overlapping communities** use a *greedy*, *agglomerative approach* and run *multiple separate seed set expansions*. Lancichinetti et al. run **greedy seed set expansions**, each with a *single seed vertex*. Overlapping communities are produced by a sequentially running expansions from a node not yet in a community. Lee et al. use **maximal cliques as seed sets**. Havemann et al. **greedily expand cliques**.
The authors of this paper discuss a dynamic approach for **community detection using seed set expansion**. Simply marking the neighbours of changed vertices is a **naive approach**, and has *severe shortcomings*. This is because *communities can split apart*. The simple updating method *may fail even when it outputs a valid community* in the graph.

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

A **community** (in a network) is a subset of nodes which are _strongly connected among themselves_, but _weakly connected to others_. Neither the number of output communities nor their size distribution is known a priori. Community detection methods can be divisive or agglomerative. **Divisive methods** use _betweeness centrality_ to **identify and remove bridges** between communities. **Agglomerative methods** greedily **merge two communities** that provide maximum gain in _modularity_. Newman and Girvan have introduced the **modularity metric**. The problem of community detection is then reduced to the problem of modularity maximization which is **NP-complete**. **Louvain method** is a variant of the _agglomerative strategy_, in that is a _multi-level heuristic_.
https://gist.github.com/wolfram77/917a1a4a429e89a0f2a1911cea56314d
In this paper, the authors discuss **four heuristics** for Community detection using the _Louvain algorithm_ implemented upon recently developed **Grappolo**, which is a parallel variant of the Louvain algorithm. They are:
- Vertex following and Minimum label
- Data caching
- Graph coloring
- Threshold scaling
With the **Vertex following** heuristic, the _input is preprocessed_ and all single-degree vertices are merged with their corresponding neighbours. This helps reduce the number of vertices considered in each iteration, and also help initial seeds of communities to be formed. With the **Minimum label heuristic**, when a vertex is making the decision to move to a community and multiple communities provided the same modularity gain, the community with the smallest id is chosen. This helps _minimize or prevent community swaps_. With the **Data caching** heuristic, community information is stored in a vector instead of a map, and is reused in each iteration, but with some additional cost. With the **Vertex ordering via Graph coloring** heuristic, _distance-k coloring_ of graphs is performed in order to group vertices into colors. Then, each set of vertices (by color) is processed _concurrently_, and synchronization is performed after that. This enables us to mimic the behaviour of the serial algorithm. Finally, with the **Threshold scaling** heuristic, _successively smaller values of modularity threshold_ are used as the algorithm progresses. This allows the algorithm to converge faster, and it has been observed a good modularity score as well.
From the results, it appears that _graph coloring_ and _threshold scaling_ heuristics do not always provide a speedup and this depends upon the nature of the graph. It would be interesting to compare the heuristics against baseline approaches. Future work can include _distributed memory implementations_, and _community detection on streaming graphs_.

Application Areas of Community Detection: A Review : NOTES

This is a short review of Community detection methods (on graphs), and their applications. A **community** is a subset of a network whose members are *highly connected*, but *loosely connected* to others outside their community. Different community detection methods *can return differing communities* these algorithms are **heuristic-based**. **Dynamic community detection** involves tracking the *evolution of community structure* over time.
https://gist.github.com/wolfram77/09e64d6ba3ef080db5558feb2d32fdc0
Communities can be of the following **types**:
- Disjoint
- Overlapping
- Hierarchical
- Local.
The following **static** community detection **methods** exist:
- Spectral-based
- Statistical inference
- Optimization
- Dynamics-based
The following **dynamic** community detection **methods** exist:
- Independent community detection and matching
- Dependent community detection (evolutionary)
- Simultaneous community detection on all snapshots
- Dynamic community detection on temporal networks
**Applications** of community detection include:
- Criminal identification
- Fraud detection
- Criminal activities detection
- Bot detection
- Dynamics of epidemic spreading (dynamic)
- Cancer/tumor detection
- Tissue/organ detection
- Evolution of influence (dynamic)
- Astroturfing
- Customer segmentation
- Recommendation systems
- Social network analysis (both)
- Network summarization
- Privary, group segmentation
- Link prediction (both)
- Community evolution prediction (dynamic, hot field)
<br>
<br>
## References
- [Application Areas of Community Detection: A Review : PAPER](https://ieeexplore.ieee.org/document/8625349)

Community Detection on the GPU : NOTES

This paper discusses a GPU implementation of the Louvain community detection algorithm. Louvain algorithm obtains hierachical communities as a dendrogram through modularity optimization. Given an undirected weighted graph, all vertices are first considered to be their own communities. In the first phase, each vertex greedily decides to move to the community of one of its neighbours which gives greatest increase in modularity. If moving to no neighbour's community leads to an increase in modularity, the vertex chooses to stay with its own community. This is done sequentially for all the vertices. If the total change in modularity is more than a certain threshold, this phase is repeated. Once this local moving phase is complete, all vertices have formed their first hierarchy of communities. The next phase is called the aggregation phase, where all the vertices belonging to a community are collapsed into a single super-vertex, such that edges between communities are represented as edges between respective super-vertices (edge weights are combined), and edges within each community are represented as self-loops in respective super-vertices (again, edge weights are combined). Together, the local moving and the aggregation phases constitute a stage. This super-vertex graph is then used as input fof the next stage. This process continues until the increase in modularity is below a certain threshold. As a result from each stage, we have a hierarchy of community memberships for each vertex as a dendrogram.
Approaches to perform the Louvain algorithm can be divided into coarse-grained and fine-grained. Coarse-grained approaches process a set of vertices in parallel, while fine-grained approaches process all vertices in parallel. A coarse-grained hybrid-GPU algorithm using multi GPUs has be implemented by Cheong et al. which grabbed my attention. In addition, their algorithm does not use hashing for the local moving phase, but instead sorts each neighbour list based on the community id of each vertex.
https://gist.github.com/wolfram77/7e72c9b8c18c18ab908ae76262099329

Survey for extra-child-process package : NOTES

Useful additions to inbuilt child_process module.
📦 Node.js, 📜 Files, 📰 Docs.
Please see attached PDF for literature survey.
https://gist.github.com/wolfram77/d936da570d7bf73f95d1513d4368573e

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

This paper presents two algorithms for efficiently computing PageRank on dynamically updating graphs in a batched manner: DynamicLevelwisePR and DynamicMonolithicPR. DynamicLevelwisePR processes vertices level-by-level based on strongly connected components and avoids recomputing converged vertices on the CPU. DynamicMonolithicPR uses a full power iteration approach on the GPU that partitions vertices by in-degree and skips unaffected vertices. Evaluation on real-world graphs shows the batched algorithms provide speedups of up to 4000x over single-edge updates and outperform other state-of-the-art dynamic PageRank algorithms.

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May.
https://gist.github.com/wolfram77/1c1f730d20b51e0d2c6d477fd3713024

Fast Incremental Community Detection on Dynamic Graphs : NOTES

In this paper, the authors describe two approaches for dynamic community detection using the CNM algorithm. CNM is a hierarchical, agglomerative algorithm that greedily maximizes modularity. They define two approaches: BasicDyn and FastDyn. BasicDyn backtracks merges of communities until each marked (changed) vertex is its own singleton community. FastDyn undoes a merge only if the quality of merge, as measured by the induced change in modularity, has significantly decreased compared to when the merge initially took place. FastDyn also allows more than two vertices to contract together if in the previous time step these vertices eventually ended up contracted in the same community. In the static case, merging several vertices together in one contraction phase could lead to deteriorating results. FastDyn is able to do this, however, because it uses information from the merges of the previous time step. Intuitively, merges that previously occurred are more likely to be acceptable later.
https://gist.github.com/wolfram77/1856b108334cc822cdddfdfa7334792a

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About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Adjusting Bitset for graph : SHORT REPORT / NOTES

Adjusting Bitset for graph : SHORT REPORT / NOTES

Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Adjusting primitives for graph : SHORT REPORT / NOTES

Adjusting primitives for graph : SHORT REPORT / NOTES

Experiments with Primitive operations : SHORT REPORT / NOTES

Experiments with Primitive operations : SHORT REPORT / NOTES

PageRank Experiments : SHORT REPORT / NOTES

PageRank Experiments : SHORT REPORT / NOTES

Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...

Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...

word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...

DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES

DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES

Shared memory Parallelism (NOTES)

Shared memory Parallelism (NOTES)

A Dynamic Algorithm for Local Community Detection in Graphs : NOTES

A Dynamic Algorithm for Local Community Detection in Graphs : NOTES

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

Application Areas of Community Detection: A Review : NOTES

Application Areas of Community Detection: A Review : NOTES

Community Detection on the GPU : NOTES

Community Detection on the GPU : NOTES

Survey for extra-child-process package : NOTES

Survey for extra-child-process package : NOTES

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

Fast Incremental Community Detection on Dynamic Graphs : NOTES

Fast Incremental Community Detection on Dynamic Graphs : NOTES

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【关于学历材料质量】
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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Wired_2.0_Create_AmsterdamJUG_09072024.pptx

In this talk, we will explore strategies to optimize the success rate of storing and retaining new information. We will discuss scientifically proven ideal learning intervals and content structures. Additionally, we will examine how to create an environment that improves our focus while you remain in the “flow”. Lastly we will also address the influence of AI on learning capabilities.
In the dynamic field of software development, this knowledge will empower you to accelerate your learning curve and support others in their learning journeys.

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回顧前一篇測試左移, 裡面有提到幾個概念, 探討如果越早且頻繁地發現軟體的缺陷, 因此開發人員可以更容易地修復問題. 且成本跟壓力都是小的. 並且幾個實際的例子引導出測試左移帶來的好處及最佳實踐.

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Software development... for all? (keynote at ICSOFT'2024)

Our world runs on software. It governs all major aspects of our life. It is an enabler for research and innovation, and is critical for business competitivity. Traditional software engineering techniques have achieved high effectiveness, but still may fall short on delivering software at the accelerated pace and with the increasing quality that future scenarios will require.
To attack this issue, some software paradigms raise the automation of software development via higher levels of abstraction through domain-specific languages (e.g., in model-driven engineering) and empowering non-professional developers with the possibility to build their own software (e.g., in low-code development approaches). In a software-demanding world, this is an attractive possibility, and perhaps -- paraphrasing Andy Warhol -- "in the future, everyone will be a developer for 15 minutes". However, to make this possible, methods are required to tweak languages to their context of use (crucial given the diversity of backgrounds and purposes), and the assistance to developers throughout the development process (especially critical for non-professionals).
In this keynote talk at ICSOFT'2024 I presented enabling techniques for this vision, supporting the creation of families of domain-specific languages, their adaptation to the usage context; and the augmentation of low-code environments with assistants and recommender systems to guide developers (professional or not) in the development process.

The Ultimate Guide to Phone Spy Apps: Everything You Need to Know

Unlock the ultimate guide to phone spy apps with our comprehensive overview! Discover everything you need to know about monitoring smartphone activities discreetly and effectively. From parental control to employee management and personal security, learn how these apps, including the renowned ONEMONITAR, can safeguard your loved ones and protect your data. Dive into essential features, choosing the right app, and ethical usage tips. Stay informed and empowered in the digital age with our in-depth guide!

TEQnation 2024: Sustainable Software: May the Green Code Be with You

In a galaxy not so far away, software development is taking on an eco-friendly twist! Join me for a journey into the world of Green Software Development, where we explore how the Force of sustainability can be harnessed to create a better, greener future for software and the planet.
We'll fly away to various topics, including:
- The Green Side of Code: Discover the fundamental principles of Green Software Development and how they can lead to reduced energy consumption, lower carbon footprints, and more environmentally responsible software.
- Eco-Jedi Tools: Explore the tools and techniques at the heart of Green Software Development, including energy-efficient coding practices and sustainable development methodologies.
- Carbon Emissions and the Dark Side: Learn about the environmental impact of software and how we can combat the "Dark Side" of excessive energy consumption with eco-conscious programming.
- Ewoks vs. Energy Efficiency: Are you building your software like the energy-efficient Ewoks or the resource-hungry Death Star?
- The Path to a Greener Future: We'll discuss the challenges and opportunities ahead on our journey toward a more sustainable software galaxy and how you can be a part of it.
Join me for an engaging and informative presentation where we combine the power of technology and the wisdom of the Jedi to bring balance to the software development Force. Together, we'll ensure that the code is green, and our planet is preserved for generations to come. May the Green Code Be with You!

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Wired_2.0_Create_AmsterdamJUG_09072024.pptx

Wired_2.0_Create_AmsterdamJUG_09072024.pptx

當測試開始左移

當測試開始左移

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Software development... for all? (keynote at ICSOFT'2024)

Software development... for all? (keynote at ICSOFT'2024)

The Ultimate Guide to Phone Spy Apps: Everything You Need to Know

The Ultimate Guide to Phone Spy Apps: Everything You Need to Know

TEQnation 2024: Sustainable Software: May the Green Code Be with You

TEQnation 2024: Sustainable Software: May the Green Code Be with You

- 1. 2013/2/12 1 1 PAGERANK ON AN PAGERANK ON AN EVOLVING GRAPH Bahman Bahmani(Stanford) Ravi Kumar(Google) Mohammad Mahdian(Google) Eli Upfal(Brown) Present by Present by Yanzhao Yang
- 2. 2013/2/12 2 Evolving Graph(Web Graph) g p ( p ) 2 The directed links between web pages The directed links between web pages Used for computing the PageRank of the WWW pages [4] pages [4]
- 3. 2013/2/12 3 Page Rank g 3 Classic link analysis algorithm based on the web Classic link analysis algorithm based on the web graph A page that is linked to by many pages receives a A page that is linked to by many pages receives a high rank itself. Otherwise, it receives a low rank. The rank value indicates an importance of a The rank value indicates an importance of a particular page. [5] Very effective measure of reputation for both web Very effective measure of reputation for both web graphs and social networks.
- 5. 2013/2/12 5 Problem 5 Traditional algorithm paradigm is inadequate for Traditional algorithm paradigm is inadequate for evolving data
- 6. 2013/2/12 6 Traditional Paradigm g 6 Stationary dataset input- inadequate for Stationary dataset input inadequate for current social networks It is necessary for algorithm to probe the Data It is necessary for algorithm to probe the input continually and produce solutions at any point in time that are close to the Al i h y p correct solution for the then-current input. Algorithm Output
- 7. 2013/2/12 7 Motivating examples g p 7 Web pages Millions of hyperlinks modified each day f f Which portions of the web should a crawler focus most? Social networks Social networks Millions of social links modified each day Which users should a third party site track in Which users should a third-party site track in order to recompute, eg, global reputation?
- 8. 2013/2/12 8 Motivating examples g p 8 In fact, Pagerank may be always imprecise. In fact, Pagerank may be always imprecise. e.g. Learn about changes-> crawling webs > crawling webs-> limit of crawling capacity-> l i f h > stale image of graph -> graph structure-> Pagerank
- 9. 2013/2/12 9 Objective Algorithm j g 9 Design an algorithm that decides which pages to Design an algorithm that decides which pages to crawl and computes the PageRank using the obtained information Maintains a good approximation of the true PageRank values of the underlying evolving graph g y g g g p Which pages to crawl The error is bounded at any point in time The error is bounded at any point in time
- 10. 2013/2/12 10 Page Rank algorithm categories g g g 10 Linear algebraic methods[3] Linear algebraic methods[3] -Power iteration speed up. E.g, web graph. E.g, web graph. Monte carlo methods[6] -efficient and highly scalable efficient and highly scalable E.g, data streaming anfd map reduce.
- 11. 2013/2/12 11 Evolving graph model g g p 11 A sequence of directed graphs over time Gt = (V, Et) = graph at time t Nodes do not change (for simplicity) A |E E | i ll Assume |Et+1 – Et| is small Choose t fine enough No change model assumed No change model assumed At time t, algorithm can probe a node u to get N(u), i.e, all edges in Et of the form (u, v) No constraints on running time or storage space Probing strategy focus on which node to probe each time
- 12. 2013/2/12 12 PageRank on evolving graphs g g g p 12 Teleport probability-ε p p y Probability of jumping to a random node Stationary distribution of random walk: -walk with ε: move to a node chosen uniformly at random -walk with 1-ε:choose one of the outgoing edges of the current node uniformly at random and move to the head of that node uniformly at random and move to the head of that edge is PageRank of node u in G t u is in-degree of node u is out-degree of node u t u in t u out
- 13. 2013/2/12 13 Baseline probing methods p g 13 Random probing(randomized) p g( ) Probe a node v chosen uniformly at random at each time step p Round-robin probing(deterministic) Cycle through all nodes and probe each in a Cy g p round-robin manner We can vary the ratio of change rate and probing y g p g rate
- 14. 2013/2/12 14 Propotional Probing p g 14 At each step t, pick a node v to probe with At each step t, pick a node v to probe with probability proportional to the PageRank of v in the algorithm's current image of the graph. g g g p The output is the PageRank vector of the current image. g
- 15. 2013/2/12 15 Priority Probing y g 15 do t step every time for 0 Priority do u node all for u
- 16. 2013/2/12 16 Experiment p 16 Dataset Dataset AS(Autonom ous Systems, graph of routers) CAIDA(communication patterns of the routers) CAIDA(communication patterns of the routers) RAND (generated randomly)
- 17. 2013/2/12 17 Experiment p 17 Random Probing serves as a baseline for Proportional Probing Round-Robin serves as a baseline for Priority Probing Hybird algorithm between Proportional Probing and Round-Robin Probing is parametrized by Metric 1 , 0 , u u t t V u t - max , L metric L t t t t L t i L t u u t t V u t - , L metric L t 1 1
- 18. 2013/2/12 18 Results( AS & CAIDA ) ( ) 18 Propotional Probing is better than Random Probing p g g Priority Probing is better than Round-Robin Probing The algorithm perform better when they probe more The algorithm perform better when they probe more frequently
- 19. 2013/2/12 19 AS graph (L1 errors) g p ( ) 19
- 20. 2013/2/12 20 AS graph (L∞ errors) g p ( ) 20
- 21. 2013/2/12 21 CAIDA graph (L1 errors) g p ( ) 21
- 22. 2013/2/12 22 CAIDA graph (L∞ errors) g p ( ) 22
- 23. 2013/2/12 23 Effect of probing rate α p g 23
- 24. 2013/2/12 24 Algorithm's image vs truth(1) g g ( ) 24
- 25. 2013/2/12 25 Algorithm's image vs truth(2) g g ( ) 25
- 26. 2013/2/12 26 Hybird Algorithm (L1 &L∞) y g ( ) 26
- 27. 2013/2/12 27 Hybird Algorithm (β=01. or 0.9) y g (β ) 27
- 30. 2013/2/12 30 Conclusion 30 Obtain simple effective algorithm Obtain simple effective algorithm Evaluate algorithms empirically on real and randomly generated datasets. randomly generated datasets. Proved theoretical results in a simplified model Analyze the theoretical error bounds of the Analyze the theoretical error bounds of the algorithm Challenge: extend our theoretical analysis to other Challenge: extend our theoretical analysis to other models of graph evolution.
- 31. 2013/2/12 31 Reference 31 1. S. Brin, L. Page, Computer Networks and ISDN Systems 30, 107 g p y (1998) 2. Glen Jeh and Jennifer Widom. 2003. Scaling personalized web search WWW '03 http://doi acm org/10 1145/775152 775191 search. WWW 03 http://doi.acm.org/10.1145/775152.775191 3. Lawrence Page, Sergey Brin, Rajeev Motwani, Terry Winograd. The PageRank Citation Ranking: Bringing Order to the Web. 4. http://en.wikipedia.org/wiki/Webgraph 5. http://en.wikipedia.org/wiki/PageRank#cite_note-1 6 K A h k N Lit k D N i k d N O i M t 6. K. Avrachenkov, N. Litvak, D. Nemirovsky, and N. Osipova. Monte Carlo methods in Pagerank computation: When one iteration is sucient. SIAM J.Numer. Anal., 45(2):890-904, 2007.