In this talk we'll explore powerful analytic techniques for graph data. Firstly we'll discover some of the innate properties of (social) graphs from fields like anthropology and sociology. By understanding the forces and tensions within the graph structure and applying some graph theory, we'll be able to predict how the graph will evolve over time. To test just how powerful and accurate graph theory is, we'll also be able to (retrospectively) predict World War 1 based on a social graph and a few simple mechanical rules.
Then we'll see how graph matching can be used to extract online business intelligence (for powerful retail recommendations). In turn we'll apply these powerful techniques to modelling domains in Neo4j (a graph database) and show how Neo4j can be used to drive business intelligence.
Don't worry, there won't be much maths :-)
Circle of Influence is a platform, built from the ground up to enable the average American citizen to browse, visualize, and understand politics in the way it is most accurately expressed: data.
When your advocacy web site has a message that is in opposition to someone else's, it can be hard to get the word out. You usually don't have the budget to advertise, and your target audience may be predisposed to not visit your site. Or they may not even be aware of the issue you are raising to even ask the right questions. This talk reviews techniques that use the programmable web (XML, KML, RSS, geocoding and microformats) to get your message in front of people via third-party websites. By allowing your data to flow freely, you can catch people's attention in unexpected ways. (Be sure to open the notes tab while viewing, and please visit my blog at http://skeptools.wordpress.com/2008/10/18/push-protesting/)
The Strength of Indirect Relations in Social Networksmosabou
Here, our goal is to develop a formal analysis of dual network structures taking into account transitive completions of higher order than the usually considered triadic closure, i.e., allowing quadruple closure and any other higher order transitive completion of open n-paths traversing two dual social networks. In this way, a sequence of indirect relations might be gen- erated in each social network, right next to the inherent direct relations in these networks. For this purpose, we introduce the setting of a “dual social network system” and we discuss how this setting might be produced in empirical situations, in which social networks are composed or partitioned in terms of various forms of actors’ categorizations from an attributional, attitudinal, typological or structural point of view. Furthermore, we are concerned with the issue of adjusting the concepts of Granovetter’s thesis on the strength of the weak ties to the case of direct and (any order) indirect relations in such dual social network systems.
Graph databases in computational bioloby: case of neo4j and TitanDBAndrei KUCHARAVY
Code used for demos is available from: https://github.com/chiffa/neo4jDemo repositry
Code used for IO over the reactome is available from: https://github.com/chiffa/PolyPharma
How To Interact With Your Front End DeveloperAll Things Open
All Things Open 2014 - Day 1
Wednesday, October 22nd, 2014
Natalie Kozlowski
Front-End Web Developer for CodeGuard
Front Dev
How To Interact With Your Front End Developer
Circle of Influence is a platform, built from the ground up to enable the average American citizen to browse, visualize, and understand politics in the way it is most accurately expressed: data.
When your advocacy web site has a message that is in opposition to someone else's, it can be hard to get the word out. You usually don't have the budget to advertise, and your target audience may be predisposed to not visit your site. Or they may not even be aware of the issue you are raising to even ask the right questions. This talk reviews techniques that use the programmable web (XML, KML, RSS, geocoding and microformats) to get your message in front of people via third-party websites. By allowing your data to flow freely, you can catch people's attention in unexpected ways. (Be sure to open the notes tab while viewing, and please visit my blog at http://skeptools.wordpress.com/2008/10/18/push-protesting/)
The Strength of Indirect Relations in Social Networksmosabou
Here, our goal is to develop a formal analysis of dual network structures taking into account transitive completions of higher order than the usually considered triadic closure, i.e., allowing quadruple closure and any other higher order transitive completion of open n-paths traversing two dual social networks. In this way, a sequence of indirect relations might be gen- erated in each social network, right next to the inherent direct relations in these networks. For this purpose, we introduce the setting of a “dual social network system” and we discuss how this setting might be produced in empirical situations, in which social networks are composed or partitioned in terms of various forms of actors’ categorizations from an attributional, attitudinal, typological or structural point of view. Furthermore, we are concerned with the issue of adjusting the concepts of Granovetter’s thesis on the strength of the weak ties to the case of direct and (any order) indirect relations in such dual social network systems.
Graph databases in computational bioloby: case of neo4j and TitanDBAndrei KUCHARAVY
Code used for demos is available from: https://github.com/chiffa/neo4jDemo repositry
Code used for IO over the reactome is available from: https://github.com/chiffa/PolyPharma
How To Interact With Your Front End DeveloperAll Things Open
All Things Open 2014 - Day 1
Wednesday, October 22nd, 2014
Natalie Kozlowski
Front-End Web Developer for CodeGuard
Front Dev
How To Interact With Your Front End Developer
Graphs for AI – Guess the Future Given the PastNeo4j
Graphs are brilliant. They're really well studied and have some amazing properties, especially for predictive analytics. But then there's AI: it's super-cool and also has some amazing abilities to guess the future given the past.
So, which are we meant to choose? I'd argue we should use both.
In this talk we'll see how graphs give us a framework for contextualizing the world around us. We'll explore how simple rules from graph theory can evolve our model to show how it might be in the future.
But that's not all, we'll also see how we can take our graphs and feed them into our ML pipelines for better scores than simple row-wise data using graph neural nets. And we'll see how to learn on graphs directly with graph convolutional networks.
Finally, we'll close the loop by asking our machine learning to tell us about other queries we should be running against the input graph to find patterns in data we don’t even know are valuable today.
To view the full-length video and tutorial, visit: https://academy.datastax.com/demos/getting-started-graph-databases
Getting Started with Graph Databases contains a brief overview of RDBMS architecture in comparison to graph, basic graph terminology, a real-world use case for graph, and an overview of Gremlin, the standard graph query language found in TinkerPop.
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The future is here and the future are Graph Databases! Have a lot of interconnected data dat you want to extract value and meaning from it? Having too many joins that are running too slow? Do you want to do Real-Time Recommendations? Read this!
GraphSummit Toronto: Keynote - Innovating with Graphs Neo4j
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Learn about the importance of graph technology, its evolution over the last few years and the impact it has had on the database and data analytics industry. This session will provide an overview of graph technology and talk about the past, present, and future of graphs and data management. Multiple use cases and customer examples will be covered, including examples of where graph databases and graph data science can assist and accelerate machine learning and artificial intelligence projects.
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Stories are critical to how humans learn, so this session will leverage a story book approach to give the audience some ideas on approaches they could use. Tim will take the audience through 3 real world examples where he has leveraged ATT&CK to drive operational improvement. The premise of each story will be real, although some of the details will be apocryphal to protect the innocent.
One story will focus on defending a network, one will look at adversary detection, while the final one will look at responding to an active attack and in each case, Tim will guide the audience to think about the kinds of data sources that ATT&CK tracks, that they might call upon to achieve a successful outcome.
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Serverless Toronto's 6th-anniversary event helps IT pros understand and prepare for the #GenAI tsunami ahead. You'll gain situational awareness of the LLM Landscape, receive condensed insights, and actionable advice about RAG in 2024 from Google AI Lead Mark Ryan and LlamaIndex creator Jerry Liu. We chose #RAG (Retrieval-Augmented Generation) because it is the predominant paradigm for building #LLM (Large Language Model) applications in enterprises today - and that's where the jobs will be shifting. Here is the recording: https://youtu.be/P5xd1ZjD-Os?si=iq8xibj5pJsJ62oW
Presented at JavaOne 2013, Tuesday September 24.
"Data Modeling Patterns" co-created with Ian Robinson.
"Pitfalls and Anti-Patterns" created by Ian Robinson.
Atelier - Architecture d’applications de Graphes - GraphSummit ParisNeo4j
Atelier - Architecture d’applications de Graphes
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Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
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Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
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Graphs are brilliant. They're really well studied and have some amazing properties, especially for predictive analytics. But then there's AI: it's super-cool and also has some amazing abilities to guess the future given the past.
So, which are we meant to choose? I'd argue we should use both.
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Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Accelerate your Kubernetes clusters with Varnish Caching
A Little Graph Theory for the Busy Developer - Jim Webber @ GraphConnect Chicago 2013
1. A Little Graph Theory for the
Busy Developer
Jim Webber
Chief Scientist, Neo Technology
@jimwebber
2. Roadmap
• Imprisoned data
• Graph models
• Graph theory
– Local properties, global behaviors
– Predictive techniques
• Graph matching
– Real-time analytics for fun and profit
• Fin
7. Aggregate-Oriented Data
http://martinfowler.com/bliki/AggregateOrientedDatabase.html
“There is a significant downside - the whole approach works really well
when data access is aligned with the aggregates, but what if you want to
look at the data in a different way? Order entry naturally stores orders as
aggregates, but analyzing product sales cuts across the aggregate structure.
The advantage of not using an aggregate structure in the database is that it
allows you to slice and dice your data different ways for different
audiences.
This is why aggregate-oriented stores talk so much about map-reduce.”
12. Property graphs
• Property graph model:
– Nodes with properties
– Named, directed relationships with properties
– Relationships have exactly one start and end node
• Which may be the same node
36. Strong Triadic Closure
It if a node has strong relationships to two
neighbours, then these neighbours must have at
least a weak relationship between them.
[Wikipedia]
39. Weak relationships
• Relationships can have “strength” as well as
intent
– Think: weighting on a relationship in a property
graph
• Weak links play another super-important
structural role in graph theory
– They bridge neighbourhoods
41. Local Bridge Property
“If a node A in a network satisfies the Strong
Triadic Closure Property and is involved in at
least two strong relationships, then any local
bridge it is involved in must be a weak
relationship.”
[Easley and Kleinberg]
43. Graph Partitioning
• (NP) Hard problem
– Recursively remove the spanning links between
dense regions
– Or recursively merge nodes into ever larger
“subgraph” nodes
– Choose your algorithm carefully – some are better
than others for a given domain
• Can use to (almost exactly) predict the
break up of the karate club!
47. Cypher
• Declarative graph pattern matching language
– “SQL for graphs”
– Columnar results
• Supports graph matching commands and
queries
– Find me stuff like this…
– Aggregation, ordering and limit, etc.
55. Wrap in a Cypher MATCH clause
MATCH (daddy)-[:BOUGHT]->()-[:MEMBER_OF]->(nappies),
(daddy)-[:BOUGHT]->()-[:MEMBER_OF]->(beer),
(daddy)-[b:BOUGHT]->()-[:MEMBER_OF]->(console)
56. Cypher WHERE clause
MATCH (daddy)-[:BOUGHT]->()-[:MEMBER_OF]->(nappies),
(daddy)-[:BOUGHT]->()-[:MEMBER_OF]->(beer),
(daddy)-[b:BOUGHT]->()-[:MEMBER_OF]->(console)
WHERE b is null
57. Full Cypher query
START beer=node:categories(category=‘beer’),
nappies=node:categories(category=‘nappies’),
xbox=node:products(product=‘xbox 360’)
MATCH (daddy)-[:BOUGHT]->()-[:MEMBER_OF]->(beer),
(daddy)-[:BOUGHT]->()-[:MEMBER_OF]->(nappies),
(daddy)-[b?:BOUGHT]->(xbox)
WHERE b is null
RETURN distinct daddy
65. What are graphs good for?
• Recommendations
• Pharmacology
• Business intelligence
• Social computing
• Geospatial
• MDM
• Data center management
• Web of things
• Genealogy
• Time series data
• Product catalogue
• Web analytics
• Scientific computing
• Indexing your slow RDBMS
• And much more!
Vanity slide - Work for Neo Technology, the commercial backer of the Neo4j open source graph database
We’ve all been there – trudging through 3 intermediate tables just to get the data you want.
We have key-value stores, typically very highly available and scalable for simple key-value data
Column stores naturally-indexed value storesContrary to common belief – Google’s big table isn’t the world’s most famous column storeBritish museum London is: it’s got columns and it’s where we stored all the stuff we nicked from the British Empire!
Aggregates:No more impedance mismatch, just decompose to documents/ k-v / columnsThe data model is less expressive than RDBMS, but that’s OK because:RDBMS constraints don’t really match application concernsThe expressiveness is in your domain logic, right?
People talk about Codd’s relational model being mature because it was proposed in 1969 – 42 years old.Euler’s graph theory was proposed in 1736 – 275 years old.
Graphs are the most natural way to model most domains. You already know this because you draw graphs on a whiteboard, but you’ve never had the opportunity to take that down into the database before.Nodes are a bit like documents, but they’re flat at present in Neo4j.You pour data into your nodes and then connect them – easy peasy.This enables high fidelity domain modeling because this is how your domains work.And you don’t have to do this stuff in your application code – it’s right there in the databaseLet’s prove it by exploring a fun domain…
Start off sketching the domain. That’s your model done – we see this when we revisit databases months after they’re been designed and put into productionNo decomposition, ER design, normalisation/denormalisation as you need with RDBMS.
Predictive analytics
Euler reduced the problem into an abstract formFrom geography to diagrammatic through to a graph.It’s the first documented case of representing the real world as a graph.
First we need to talk about some local properties
A triadic closure is a local property of (social) graphs whereby if two nodes are connected via a path involving a third node, there is an increased likelihood that the two nodes will become directly connected in future. This is a familiar enough situation for us in a social setting whereby if we happen to be friends with two people, ultimately there's an increased chance that those people will become direct friends too, since by being our friend in the first place, it's an indication of social similarity and suitability. It’s called triadic closure, because we try to close the triangle.
We see this all the time – it’s likely that if we have two friends, that they will also become at least acquaintances and potentially friends themselves!In general, if a node A has relationships to B & C then the relationship between B&C is likely to form – especially if the existing relationships are both strong.This is an incredibly strong assertion and will not be typically upheld by all subgraphs in a graph. Nonetheless it is sufficiently commonplace (particularly in social networks) to be trusted as a predictive aid.
Sentiment plays a role in how closures form too – there is a notion of balance.
From a triadic closure perspective this is OK, but intuitively it seems odd.Cartman’s friends shouldn’t be friends with his enemies. Nor should Cartman’s enemies be friends with his friends.
This makes sense – Cartman’s friend Craig is also an enemy of Cartman’s enemy TweekTwo negative sentiments and one positive sentiment is a balanced structure – and it makes sense too since we gang up with our friends on our poor beleaguered enemy
Another balanced – and more pleasant – arrangement is for three positive sentiments, in this case mutual friends.
A starting point for a network of friends and enemiesRed links indicate enemy of relationshipBlack links indicate friend of relationshipThe Three Emperor’s league
Italy forms the with Austria and Germany – a balanced +++ triadic closureIf Italy had made only a single alliance (or enemy) it would have been unstable and another relationship would be likely to form anyway!Triple Alliance
Russia becomes hostile to Austria and Germany – a balance --+ d triadic closure becomes agnostic towards France.German-Russian Lapse
The French and Russians ally, forming a balanced --+ triadic closure with the UKFrench-Russian Alliance
The UK and France enter into the famous Entente CordialeThis produces an unbalanced ++- triadic closure with Russia, and the graph doesn’t like it.
The British and Russians form an alliance, thereby changing their previously unbalanced triadic closure into a balanced one.Other local pressures on the graph make other closures form.Italy becomes hostile to Russia, forming a balanced --+ closure with the France, and another balanced --+ closure with the UK.Germany and the UK become hostile forming a balanced --+ closure with Austria and another balanced --+ closure with ItalyBritish-Russian Alliance
That WWI can be predicted without domain knowledge by iterating a graph and applying local structural constraints is nothing short of astonishing to me.Note how the network slides into a balanced labeling — and into World War I.
In this case the string triadic closure property still holds – though it is a weak link that characterises the relationship between Stan and Cartman.Given a starting graph, we can apply this simple local principal to see how it would evolve.
In this case the string triadic closure property still holds – though it is a weak link that characterises the relationship between Stan and Cartman.Given a starting graph, we can apply this simple local principal to see how it would evolve.
A local bridge acts as a link – perhaps the only realistic link - between two otherwise distant (or separate) subgraphs.Local bridges are semantically rich – they provide conduits for information flow between otherwise independent groups.In this case DATING is a local bridge – it must also be a weak relationship according to our definition of a local bridgeIntuitively this makes sense – your girl/boyfriend is rather less important at age 8 than your regular friends, IIRC.
How do we identify local bridges? Any weak link which would cause a component of the graph to become disconnected.Being able to identify local bridges is important – in this case it’s the only know conduit to allow the girls and boys to communicate.In real life local bridges are apparent in your organisation as experts (or managers); appear as nexus in fraud cases;
Zachary in the Journal of Anthropological Research 1977Intuitively we can see “clumps” in this graph.But how do we separate them out? It’s called minimum cut.
What’s interesting is that it’s mechanical – no domain knowledge is necessary. There’s only one failure with the method Zachary chose to partition the graph: node 9 should have gone to the instructor’s club but instead went with the original president of the club (node 34).Why? Because the student was three weeks away from completing a four-year quest to obtain a black belt, which he could only do with the instructor (node 1)Other minimum cut approaches might deliver slightly different results, but on the whole it’s amazing you get such insight from an algorithm!
But is there enough information in the graph itself to predict the schism?
But is there enough information in the graph itself to predict the schism?
We can use graph matching to look for behavioural patterns in the graph too!
The insight here is that we have a typical young father who buys beer, nappies and a game console simply by reducing subgraphWe have a pattern to search for
Now we look for young fathers – implied by beer and nappies purchases – who haven’t bought a game console.
Note that Max de Marzireimplemented a functionally better Graph Search with Neo4j and some Ruby gems for language processing in a weekend!
Image: real-timeBeing able to look for all young fathers who might be tempted to buy a new game console is helpful, but not dramatically different from what we have nowIt’s much faster to process in a graph, but still a latent business activity (e.g. mailshot)But you can take the same idea and run the query in real time foran individual customer at they go through the checkout!And you can do more too – you can add in more/less dimensions to the search.Does the shop have stock?Does the young father live in the right target area (r-tree, postcode)?We can vary the number of dimensions we include to tailor the search for performance/accuracy very easily in a graph – query latency is proportional to the amount of graph searched, not data set size.