- Graph Visualisation in 2018 and Beyond
Tools to visualise connected data have come a long way from the static GraphViz diagrams of yesteryear. In this talk Christian Miles of Cambridge Intelligence will cover a brief history of the various visualisation tools of the last 30 years before discussing modern, browser-based approaches to visualising graph data. Don't miss it!
- Coding practical graph applications with Neo4j & Node.js
Greg Marlin is the Founder of Transformation.ai a digital transformation software which is part of the Neo4j Startup Program. In this talk, Greg will walk us through a live coding session of Neo4j queries in Node JS. Subjects covered will include an introduction to coding Neo in Node, getting up and running quickly, adding Nodes and Relationships, common practical queries in Node, building an API for a front-end application and experiences of using Neo4j in full-stack application using a MyNERN stack (MySQL, Neo, Express, React, Node).
- An Overview of Amazon Neptune
This month Antoine Généreux, Solutions Architect at Amazon will be talking to us about one of Amazon's newest creations: Neptune. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets for uses cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security. Come learn about Neptune’s features and how you can reduce your time-to-graph by easily setting up a database and importing your property graphs and RDFs.
- Merkle DAGs: Efficient graphs for P2P networks
The Peer To Peer space is really busy these days, and Merkle DAGs (Directed Acyclic Graphs) are the core data type it's all based on. This month Georgiy Shibaev is going to take us through what a Merkle DAG is and how they're used to make distributed P2P applications like Bittorent and cryptocurrencies. See you there!
- R/IGraph workshop with Katya Ognyanova
This month Katya Ognyanova, Data Scientist and Assistant Professor at the School of Communication and Information at Rutgers University, is going to give us a workshop on using iGraph, a network analysis package for R. This workshop will cover basics of network analysis and visualization with the R package igraph (igraph.org/r). Bring your laptops and please make sure you have R (https://cran.r-project.org/), Rstudio (https://www.rstudio.com/) and iGraph (http://igraph.org/r) installed! See you there!
- Albert-László Barabási: Taming Complexity
This month we have one of the biggest names in the world of Network Science: Albert-László Barabási from the Center of Complex Networks Research, Northeastern University and Division of Network Medicine, Harvard University. His talk: "Taming complexity: From Network Science to Network Control" "Our biological existence, our ability to communicate, to exchange goods and values, are guaranteed by numerous invisible networks, from the protein and genetic networks in our cells to the world wide web, Internet and financial and trade networks. I will show that the amazingly complex topology of these highly interconnected networks are the result of self-organizing processes governed by simple but generic laws. The ultimate proof of our understanding these complex systems is reflected in our ability to control them. I will therefore explore the controllability of an arbitrary complex network, identifying the set of driver nodes whose time-dependent control can guide the system’s entire dynamics. By applying these tools to real networks, helps us unveil how the network topology determines controllability. Finally, I will discuss how network control informs our ability to predict neurons involved in specific processes in the brain, offering an avenue to experimentally test the predictions of network control." Don't miss it!
- Scale-free networks are rare
This month Anna Broido, a graduate student in applied mathematics at University of Colorado at Boulder is going to talk about her recent paper: "Scale-free networks are rare": "A central and controversial claim in modern network science is that real-world networks are typically "scale free," meaning that the fraction of nodes with degree k follows a power-law distribution. However, empirical evidence for this belief is lacking in the literature. We performed the first large-scale test of the universality of scale-free structure by applying state-of-the-art statistical tools to a large corpus of nearly 1000 network data sets drawn from a variety of different network domains. We assessed the goodness-of-fit of the power-law model, and compared this fit with alternative models to categorize how well "scale free" describes each network dataset. Overall, we found weak evidence to support the idea that most real-world networks are scale free." You can read her paper here: https://arxiv.org/pdf/1801.03400.pdf See you then!
- Handling Billions of Edges in a Graph Database
This month we have Matthew Von-Maszewski the Senior Architect at ArangoDB talking to us about scaling a distributed graph database: "The complexity and amount of data rises. Modern graph databases are designed to handle the complexity but not the amount of data. When hitting a certain size of a graph, many dedicated graph databases reach their limits in vertical or, more likely, horizontal scalability. In this talk I will provide a brief overview about current approaches and their limits towards scalability. Dealing with complex data in a complex system doesn’t make things easier… but more fun finding a solution. Join me on a journey to handle billions of edges in a graph database." I hope to see you all there!
- Graph data night
We will be kicking off 2018 with a night of applying some of the stuff we've been learning about. We'll start with just enough iGraph to be dangerous and then trying to apply our knowledge to one of a couple of data sets. Learn some R or wield your weapon of choice and let's see what we come up with. Bring your laptops, and install R/Rstudio beforehand. See you there!
- How Graphs Changed The Way Hackers Attack
BloodHound is a tool that uses graph theory to reveal hidden relationships and attack paths in an Active Directory environment. This month we will have Andy Robbins, one of the creators of Bloodhound is going to talk to us about "How Graphs Changed The Way Hackers Attack": In April of 2015, John Lambert illustrated why hackers consistently defeat network security measures, stating: "Defenders think in lists. Attackers think in graphs. As long as this is true, attackers win." One year later, Rohan Vazarkar, Will Schroeder, and I released BloodHound at the DEF CON 24 hacker convention. BloodHound is a free and open source tool that uses graph theory to show how attackers breach and take over modern corporate network. Since its release, BloodHound has changed how professional offensive consultants and network defenders view these attack paths, using Neo4j to discover in seconds what used to take days or weeks manually. With some information about the network? Who's logged in where? Who can administer what? Who's in what groups? Who has control over what objects? We can model how attackers choose their targets. The BloodHound attack graph exposes the hidden and often unintended relationships that may lead to Domain Admin, the keys to the kingdom in almost every corporate network in the world. In this talk, we will show, with live demonstrations, the full history and evolution of BloodHound, starting with the frustrations of hacking without an attack graph, covering the spark that led us to an automated graph theory approach, building upon existing tools and tradecraft to create BloodHound, and capping off with BloodHound's newest improvements, schema additions, and future features. Finally, see how defenders use BloodHound to gain critical insights from the attack graph were the good guy kind of hackers after all. Don't miss it!