GraphNews

This year we organize Graph ML track at Data Fest 2020. It's like a workshop at the conference, but more informal. We will have videos from amazing speakers and also networking, where you can talk to me, speakers, or other people who are interested in graph machine learning. pic.twitter.com/kTrJsc7ca2— Sergey Ivanov (@SergeyI49013776) September 17, 2020

Industry Applications of Network Science and Graph Algorithms : from @Tamer_Khraisha https://t.co/i4RbHEVokk#networkscience #graphalgorithms #graphdatabase #graphtheory #graphdatabase pic.twitter.com/KktQ4GGmyc— Graph Day (@GraphDay) September 16, 2020

Knowledge Graphs and Big Data Processing : From @ValentinaJanev @hajiraajabeen @DGraux Emanuel Sallingerhttps://t.co/2hLlLXwZ1l#knowledgegraphs #bigdata #graphdatabase #graphdatabases #knowledgegraph pic.twitter.com/Mo46l3yj4p— Graph Day (@GraphDay) September 16, 2020

Here's a nice demo video with real-world examples of various simple graph analytics over an RDF-based knowledge graph that leverage the RDF*/SPARQL* approach, including a brief intro to knowledge graphs and RDF*/SPARQL*. https://t.co/xQKin9PA5w— Olaf Hartig (@olafhartig) September 13, 2020

Understand parallels between recent works on latent graph learning and older techniques of manifold learning @mmbronstein #GaphNeuralNetworks #ManifoldLearning https://t.co/IzgXseBNL3 pic.twitter.com/ygeUte7kKZ— Experfy (@Experfy) September 21, 2020

"Covid-on-the-Web: Knowledge Graph and Services toAdvance COVID-19 Research" a dataset comprising twomain knowledge graphs, including named entities linked to @DBpedia, @Wikidata and other @BioPortal vocabularies.(Michel et al, 2020)https://t.co/iZJH9Y2PpV pic.twitter.com/7hY0QpUQfv— WikiResearch (@WikiResearch) September 21, 2020

"To utilize document-centric XML data linked from entities in LOD, in this paper, a SPARQL-based seamless access method on RDF and XML data is proposed." > SPARQL with XQuery-based Filtering https://t.co/Jz0SrfLFA6 pic.twitter.com/rhsWPEXOyb— Aaron Bradley (@aaranged) September 15, 2020

The ICIJ used Neo4j to expose a vast money laundering network running through Western banks. This blog analyzes the FinCEN Files data.

The Research team at the @Wikimedia Foundation will give an overview on the first draft of the taxonomy of knowledge gaps in Wikimedia projects. You can read more about the taxonomy here: https://t.co/8eAj6mpvIP pic.twitter.com/P4p29vJO7i— WikiResearch (@WikiResearch) September 22, 2020

"What’s taking the world by storm today (...) is something very different. It is #bigdata and layers and layers of complex mathematics used to solve very specific(...) problems." -Andrea Malick. See how #knowledgegraphs are "adding the human factor" to #AI https://t.co/oQsGkJ00pQ pic.twitter.com/DlUP7vB2qH— Carbon LDP (@CarbonLDP) September 23, 2020

Use #gremlin to find the shortest path between nodes in a #graph. Checkout this Gremlify workspace:#graphdb #gremlinhttps://t.co/qYZb5EbaVL— gremlify.com (@gremlify) September 24, 2020

"#Neo4j announced a Knowledge Graph Quick Start program to support the company’s rapidly growing knowledge graph customer base."Learn more about the Knowledge Graph Quick Start program: https://t.co/Bnqo6O9hbW#knowledgegraphs #neo4j #graphtechnology— Neo4j (@neo4j) September 24, 2020

Community detection by label propagation using @graphileon and built-in #graphalgorithms in @Neo4j. A demo (https://t.co/zdY3wnc4YC), and why we think that #YourAppIsAGraph is the new way of building graphy applications. @nodejs #lowcoding. pic.twitter.com/YzBP3vCE2D— Graphileon (@graphileon) September 24, 2020

"RDF2Vec Light – A Lightweight Approach for Knowledge Graph Embeddings"(Portisch et al, 2020)paper: https://t.co/6kt4P3p74Oproject page: https://t.co/q1nXGR8jfc pic.twitter.com/11QxcOBFEI— WikiResearch (@WikiResearch) September 24, 2020

In the intricate play b/n #metadata, #ontologies & #textanalysis, each has an important role in capturing the multiplicity & complexity of data relationships. When we talk about #semantics & #knowledgegraphs, this is what we mean. https://t.co/0LznDdGry6— Ontotext (@ontotext) September 24, 2020

We're releasing everything on StarE ⭐️ - a #GNN encoder for hyper-relational #KnowledgeGraph techniques like RDF* and LPG. Have fun 😊Blog: https://t.co/OV2FprqzJtPaper: https://t.co/K49xGI6MI7Code: https://t.co/mrwyvXoPMfReport @weights_biases : https://t.co/KFsxZyiw31 https://t.co/NfAndJ5eSs— Michael Galkin (@michael_galkin) September 24, 2020

Cool! RDF* support in Neo4j's RDF plug in. https://t.co/f7t2Iyo8rY— Olaf Hartig (@olafhartig) September 24, 2020

“How do you select a graph database? Learn how in @itworldca. Read here: https://t.co/Z529PWdeW6 | #GraphDatabase #GraphDB #GraphAnalytics #DataScience #Developer #Analytics #BigData”

New blog entry: Using SPARQL do to quick and dirty joins of CSV data https://t.co/BVvuY4Xlhv— Bob DuCharme (@bobdc) September 27, 2020

Finding buried treasure: article on the role of graph databases in HR & HCM (Human Capital Management) #KnowledgeGraphs#Datahttps://t.co/pZB7PuDco9 pic.twitter.com/mQqH2Drhqy— Synaptica LLC (@Synaptica) September 25, 2020

@AllianzBenelux - "we’ve made €2m profit using graph technology"Read @diginomica's full story here: https://t.co/BByFtdRRyq#Neo4j #GraphDatabases #FraudDetection #FinTech— Neo4j (@neo4j) September 25, 2020

“My post on Mount Rainier’s Laplacian (https://t.co/ONdkZJdayX) is a 101 intro to aspects of spectral graph theory. This great talk by @mmbronstein shows how this theory also forms the basis of deep learning on graph-like structures. Read at least 5 papers today because of it. https://t.co/eMsV9...

TopQuadrant CEO, Irene Polikoff, provides an overview of the two main graph models along with illustrations of their similarities and differences in graph diagrams in Part I of II in this article series from @TDAN_com https://t.co/CxOrTb3ELL#knowledgegraphs #datagovernance— TopQuadrant (@TopQuadrant) September 25, 2020

Another exciting application for #graphml With @frederickmonti back in 2018 we have had our excursion into HEP working on neutrino detection with @uw_icecube https://t.co/roiByGczpi— Michael Bronstein (@mmbronstein) September 25, 2020

The discussion on personal #knowledgegraphs is gaining traction. @kurt_cagle had it listed among other most common use cases for knowledge graph application: https://t.co/jLpqZOWJjrhttps://t.co/xxjnlAtti2— Ontotext (@ontotext) September 25, 2020

Knowledge Graphs at a glance by @giuseppe_futia in @TDataScience https://t.co/q5W3CLUXSj— Teodora Petkova (@TheodoraPetkova) September 28, 2020

I'll be discussing "Graph Queries with Gremlin Language Variants" at the Category Theory and Applications group meetup on October 6: https://t.co/MG1HpNEiGd Be prepared to see Gremlin in many different forms! #graphdb pic.twitter.com/OIOsfLvWze— stephen mallette (@spmallette) September 28, 2020

"Therefore, the job of data scientists is to decode the data and to find the knowledge encoded in the data—i.e., to find the model in the data, because the data is the model." - @KirkDBorne, @BoozAllen https://t.co/sZJzBkiA1p pic.twitter.com/NpKaDLLUuL— Kirk Borne (@KirkDBorne) September 28, 2020

😉Happy to share our work on multi-hop (open-domain) QA (https://t.co/7BhmCqX64m). TL;DR: you don't need the Wikipedia hyperlinks to achieve SoTA performance on HotpotQA(@qi2peng2 )! A shared RoBERTa encoder (for both Q and docs) is all you need to retrieve SP passages! 1/3 pic.twitter.com/B9JO85Vd1l— Wenhan Xiong (@xwhan_) September 29, 2020