The dynamic face-to-face interaction networks represent the interactions that happen during discussions between a group of participants playing the Resistance game. This dataset contains networks extracted from 62 games. Each game is played by 5-8 participants and lasts between 45--60 minutes. We extract dynamically evolving networks from the free-form discussions using the ICAF algorithm. The extracted networks are used to characterize and detect group deceptive behavior using the DeceptionRank algorithm.

The networks are weighted, directed and temporal. Each node represents a participant. At each 1/3 second, a directed edge from node u to v is weighted by the probability of participant u looking at participant v or the laptop. Additionally, we also provide a binary version where an edge from u to v indicates participant u looks at participant v (or the laptop).

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.

The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.

http://snap.stanford.edu/data/index.html#face2face

DrugProt Silver Standard Knowledge Graph

Please cite if you use any DrugProt resource:

Antonio Miranda-Escalada, Farrokh Mehryary, Jouni Luoma, Darryl Estrada-Zavala, Luis Gasco, Sampo Pyysalo, Alfonso Valencia, Martin Krallinger, Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical–protein relations, Database, Volume 2023, 2023, baad080

@article{miranda2023overview, title={Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical--protein relations}, author={Miranda-Escalada, Antonio and Mehryary, Farrokh and Luoma, Jouni and Estrada-Zavala, Darryl and Gasco, Luis and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin}, journal={Database}, volume={2023}, pages={baad080}, year={2023}, publisher={Oxford University Press UK} }

Miranda, Antonio, et al. "Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations." Proceedings of the seventh BioCreative challenge evaluation workshop. 2021.

@inproceedings{miranda2021overview, title={Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations}, author={Miranda, Antonio and Mehryary, Farrokh and Luoma, Jouni and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin}, booktitle={Proceedings of the seventh BioCreative challenge evaluation workshop}, year={2021} }

Description

Files:

• drugprot-silver-standard-kg.zip : JSON files with the relations predicted by the DrugProt systems and their precision
• large_scale_network_abstracts.tsv : PubMed abstracts
• large_scale_network_entities.tsv : CHEMICAL/drug and GENE/protein entities predicted by DrugProt NER Taggers
• large_scale_network_pmids.txt : list of PMIDs

Related resources:

• Web
• DrugProt corpus
• Evaluation library
• Online evaluation (CodaLab)
• Relation annotation guidelines
• Gene and protein annotation guidelines
• Chemicals and drugs annotation guidelines
• FAQ
• DrugProt Large Scale Additional SubTrack
• DrugProt Large Scale document collection protocol