Graph Database Tokyo

47th Neo4j Users Group Tokyo Meetup via ZOOM Friday, May. 31, 2023

Session

• Linkurious: Graph visualization and analysis solutions

Linkuriousは、あらゆるタイプのユーザーがグラフ解析の力を利用できるようにするためのソリューションを開発してきました。
これらのソリューションを使用すると、データベースの探索、分析、視覚化、およびクエリを簡単に行うことができます。
また、必要に応じて高度なカスタマイズが必要な場合は、独自のグラフ可視化アプリケーションを作成するために当社のソリューションを使用することもできます。
当社の製品は、サプライチェーンや製造、サイバーセキュリティ、インテリジェンス、金融犯罪、データガバナンスとIT、ライフサイエンスなど、多様な用途に使用できるため、さまざまな異なる業界の顧客がいます。
このプレゼンテーションでは、会社概要、提供している製品、特定のユースケースに焦点を当てたデモを行います。
最後にQ&Aセッションを行います。

Linkurious has developed solutions to enable any type of user to have access to the power of graph analytics.
With our solutions, you can explore, analyze, visualize, and query databases very easily.
You can also use our solutions to create your own graph visualization application if you need high customization.
We have customers within many different industries since you can use our products for many different use cases such as Supply Chain and manufacturing, Cybersecurity, Intelligence, Financial Crime, Data governance & IT, or Life Sciences.
During this presentation, you can expect a description of the company, the different products offered, a focus on specific use cases as well as a demo for each product.
Finally, there will be a Q&A session at the end.

Nels Biagui, Business Developer at Linkurious
https://linkurious.com/

• GDSを使用してSlackのインフルエンサーを割り出す
  Find Slack influencers with the GDS PageRank algorithm

Neo4jによるデータ分析の知見を得るため、社内のSlackのデータを元に、Neo4jを使用したデータ分析を試してみました。
SlackのデータをAPIで取得してNeo4jのグラフデータにし、それを元にGDSの中心性解析アルゴリズム(PageRank)を使用して影響力のある発言者を割り出しました。

直感に反しない結果が得られたので、GDSを使用した分析のほんの一例としてご参考になるかと思い発表します。

To gain insight into data analysis with Neo4j, we tried to analyze data using Neo4j based on internal Slack data.
We retrieved Slack data via API, turned it into Neo4j graph data, and then used it to determine influential speakers using GDS's centrality analysis algorithm (PageRank).
The results were not counter-intuitive, so I present this as an example of an analysis using GDS for your reference.

荒井 創 (ARAI Hajime)
クリエーションライン株式会社
https://www.creationline.com/

【How to join the Neo4j Users Group Meetup】

Go to this URL and Get Zoom Link Info

https://jp-neo4j-usersgroup.connpass.com/event/284025/

Zoom Passcode
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This is the second session as part of the training series.

Register here: https://go.neo4j.com/TR230601IntermediteCypherandDataModelling_RegistrationPage.html

Session #2: Intermediate Cypher and Data Modelling

Cypher
Learn everything there is to know to query Neo4j, including the more advanced cypher functionality, APOC, and everything in between.

Data Modelling
What is a graph data model? Modeling nodes and creating nodes for an instance model. Modeling relationships and creating relationships for an instance model.

This is the third session as part of the training series.

Register here: https://go.neo4j.com/TR230614KnowledgeGraphswithChatGPT_RegistrationPage.html

Session #3: Knowledge Graphs with ChatGPT
ChatGPT seems fascinating?
In this session, we will do analysis of the OpenAI engine to produce meaningful output from the text in the form of a knowledge graph using Neo4j.

We are super excited to bring the 4th episode of Graphversation, a monthly livestream series that brings graph experts and Neo4j ninjas for engaging and informative conversations about the fascinating world of graphs. Whether you are a beginner or an advanced developer in graphs, we are confident that you will find our conversations engaging, informative, and thought-provoking.

In this episoide, we have Dinesh Venkatesan who will talk about, "Causal inference powered by Knowledge Graph for applied security research". Dinesh will talk about approaching the security research problem as a causal query that can aid binary analysis targeted to run on cross platform operating systems, specifically Linux, Android, macOS, and Windows.

The first part of the talk would cover different vantage points from which the execution of the executable is observed and highlights the novelty in creating actionable threat intel and analysis artifacts used for inference in the second part of the talk. Three main vantage points are:

• Observing the events via Kernel Modules/extensions/drivers
• Memory forensics artifacts powered by Volatility
• Modern observability frameworks (eBPF, PINtrace)

The observations taken from these vantage points are stored as a Graph Structured Data. To percolate the raw data transform into information and inturn transform into Knowledge. Aided by Graph analytics and domain expertise expressed in graph query language, the framework then provides a central causal inference engine that allows the researchers to get the right information at the right time to understand the causal relationship among the variants. In addition to that the framework can also act as a search interface for the researchers to look for specific patterns and yields additional threat intelligence in the form of a recommender system.

Join us live on 19th June on YouTube at 10 AM IST: https://www.youtube.com/watch?v=e3svI7dHIbY (Don't forget to click the bell icon to be notified when we go live)

Speaker:
Dinesh Venkatesan, Security Researcher @ Microsoft
Dinesh Venkatesan is a Logician & Mathematician presently working as security researcher at Microsoft. He has been in the cybersecurity industry for over 17 years working with Google, Symantec and HCL Technologies and has published numerous blog posts on malware analysis. He is a specialist on the mobile threat landscape and desktop security threats and has discovered multiple vulnerabilities in Android framework layer, responsibly reporting it to Android and helping to make the OS secure. He has hands-on expertise in writing generic detection and cure routines for prevalent malware families. He is on an active look out for collecting threat intel about sophisticated attacks and keen on researching various threat actors and developing useful insights into malware evolution. https://www.linkedin.com/in/dinesh-v-a5922726/

Host:
Siddhant Agarwal, Developer Relations APAC @ Neo4j
With over a decade of industry experience, Siddhant has literally spent his entire career in building, scaling and growing communities in India & APAC and has found his passion in launching ed-tech initiatives, design innovation, growing startup ecosystem and building for the next billion users. Siddhant has previously worked with Open Financial Technologies, Google, Beahead and IBM. A design thinker at heart, he loves working with startups and helping them scale in UX and improve their designs. https://www.linkedin.com/in/sidagarwal04