To do

• Download
• URLs
• Edit

An interesting source about the Microsoft Academic Graph (MAG) and other bibliographic databases is this Master Thesis. Scholarly data

Some information about downloading MAG is available at the Stackoverflow:

• http://ma-graph.org/
• https://zenodo.org/record/2628216#.Xgo1slVKjIU Download MAG

Two network data sets: Coauthorship and Citation networks for statisticians. The data sets are based on all research papers published in four of the top journals in statistics from 2003 to the first half of 2012.

https://github.com/bavla/biblio/wiki/JiJin

Haiko Lietz collected and cleaned a data set on SNA till 2012

• https://data.gesis.org/sharing/#!Detail/10.7802/1437
• https://data.gesis.org/sharing/#!Detail/10.7802/1520

Compare with our dataset - stability of results.

C:\Users\batagelj\Downloads\data\leitz

https://www.aminer.cn/citation

The data set is designed for research purpose only. The citation data is extracted from DBLP, ACM, MAG (Microsoft Academic Graph), and other sources. The first version contains 629,814 papers and 632,752 citations. Each paper is associated with abstract, authors, year, venue, and title.

The data set can be used for clustering with network and side information, studying influence in the citation network, finding the most influential papers, topic modeling analysis, etc.

DBLP-Citation-network V11: 4,107,340 papers and 36,624,464 citation relationships (2019-05-05)

Complementary analysis to the paper.

From: “shamik_sharma” shamik@rockyou.com
Date: Sat, March 1, 2008 03:29
To: ucinet@yahoogroups.com

Hi all,

I am new to this group…

I am looking for existing research on a particular problem.

Say, you have a social network where edge-weights indicate probability of information flow. You have to pick N nodes to which you give the information. Which N nodes would you pick to have maximal information dissemination ?

Just picking the nodes with highest centrality may not be best because two nodes may be part of the same subgroups and overlap in the people the information spreads to.

Any pointers would be very welcome…

Thanks! Shamik Sharma

Shamik See Steve Borgatti's paper

Borgatti, S. (2006). Identifying key players in a social network. Computational and Mathematical Organization Theory, 12, 21-34.

And the key player software that comes with UCINET, you might also consult:

Valente, T.W., Hoffman, B.R., Ritt-Olson, A., Lichtman, K., & Johnson, C.A. (2003). The effects of a social network method for group assignment strategies on peer led tobacco prevention programs in schools. American Journal of Public Health. 93, 1837-1843.

- Tom

Thomas W. Valente, PhD

Kaj veš o problemih:

1. določi k točk, tako da bodo pokrivale čimveč drugih.
2. določi k toèk, tako da:
   1. vsota dolžin najkrajših poti do najbližje najmanjša
   2. najdaljša med njimi čim krajša

4. 10. 2015

http://mathinsight.org/small_world_network

Mogoč je drug pogled na Watts-Strogatz: razvrstimo vozlišča, dobimo ustrezno permutacijo in omrežje krožno prikažemo. Pogledamo koliko je bližnjic.

4. 10. 2015

Ali bi se dalo posplošiti pristop s polgrupami (Pattison) na polkolobarje (ali sorodno strukturo)?

• http://moreno.ss.uci.edu/72.pdf
• http://eclectic.ss.uci.edu/~drwhite/pw/Galois.pdf
• http://ceur-ws.org/Vol-871/paper_4.pdf
• http://www.egc.asso.fr/sdoc-264-egc2013_conf_inv_Missaoui.pdf
• http://www.coalitiontheory.net/content/lattices-social-networks-influence
• https://books.google.si/books?id=DXWLBQAAQBAJ
• http://www.complexnetworks.fr/social-networks-conceptual-analysis/
• http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4295369&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4295369
• http://www.softcomputing.net/wi09.pdf
• http://www.nature.com/articles/srep10265
• http://www.au.af.mil/info-ops/theory.htm

10. October 2020

Кахатт Ваккари Яфэт Франсиско / Francisco Kajatt-Vaccari - ykajattvaccari@edu.hse.ru

1. Disambiguation (identification, entity resolution) problem in construction of bibliographic networks. See pages 1091-1092 and Appendix A on page 1108 in

• Maltseva, D., Batagelj, V.: Social network analysis as a field of invasions: bibliographic approach to study SNA development. Scientometrics, 121(2019)2, 1085-1128. PDF

or page 31 in https://arxiv.org/pdf/1812.05908.pdf .

2. Analysis of bike sharing systems.

• wiki:bikes
• Bikes slides
• analysis of personal patterns

3. Temporal network analysis.

• Batagelj, V., Praprotnik, S.: An algebraic approach to temporal network analysis based on temporal quantities. Social Network Analysis and Mining, 6(2016)1, 1-22 PDF
• Batagelj, V., Maltseva, D.: Temporal bibliographic networks. Journal of Informetrics, Volume 14, Issue 1, February 2020, 101006 PDF
  

Advanced topic: Clustering in temporal networks AND/OR Searching for temporal patterns (motifs). Analysis of (temporal) networks obtained from selected data set (one of):

1. IMDB https://www.imdb.com/, https://www.imdb.com/interfaces/ , https://ieeexplore.ieee.org/document/4126213
2. DBLP https://dblp.org/ , https://dblp.org/faq/1474679.html
3. KEDS http://eventdata.parusanalytics.com/
4. MAG https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/
5. US Patents http://patft.uspto.gov/netahtml/PTO/srchnum.htm
6. GDELT https://www.gdeltproject.org/
7. maybe also (I would need to drill in the data): http://seshatdatabank.info/datasets/ , http://seshatdatabank.info/databrowser/

Oct 2020

Preglej metode iz

Michel Callon, Jean-Pierre Courtial, William A. Turner, Serge Bauin: From translations to problematic networks: An introduction to co-word analysis. Social Science Information Volume: 22 issue: 2, page(s): 191-235 Issue published: March 1, 1983 https://doi.org/10.1177/053901883022002003

Zhu, X., Zhang, Y.: Co-word analysis method based on meta-path of subject knowledge network. Scientometrics 123, 753–766 (2020). https://doi.org/10.1007/s11192-020-03400-0 https://link.springer.com/article/10.1007/s11192-020-03400-0

Ali bi se dalo uporabiti pri analizi razvojnih 'front'?

Nov 1, 2020

Gábor Csárdi, Tamás Nepusz, Edoardo M. Airoldi Statistical Network Analysis with igraph variation of information p. 113

Nov 5, 2020

• copula → criterion
• Louvain & Brounoghue

Advances in Data Analysis and Classification: ADAC-D-20-00208: “Statistical Independence” versus “Logical Indetermination”, two ways of generating clustering criteria through couplings : Application to graphs modularization

• Conde-Cespedes, P.: Modelisations et extensions du formalisme de l'analyse relationnelle mathematique a la modularisation des grands graphes. Ph.D. thesis, Paris 6 (2013)
• F. Marcotorchino1 and P. Conde C´espedes: Optimal Transport and Minimal Trade Problem, Impacts on Relational Metrics and Applications to Large Graphs and Networks Modularity

Nov 5, 2020

John McLevey, Reid McIlroy-Young: Introducing metaknowledge: Software for computational research in information science, network analysis, and science of science. Journal of Informetrics 11 (2017) 176–197 www.elsevier.com/locate/joi

Nov 5, 2020

https://worldwidescience.org/topicpages/g/graph+decomposition+technique.html

• Penghang Liu, Valerio Guarrasi, Ahmet Erdem Sarıyüce: Temporal Network Motifs: Models, Limitations, Evaluation. https://arxiv.org/pdf/2005.11817.pdf
• New paper with my student Penghang Liu: 'Analysis of Core and Truss Decomposition on Real-World Networks', to appear in MLG workshop (in conj. with SIGKDD'19).
• Penghang Liu, Erdem Sarıyüce: Characterizing and Utilizing the Interplay Between Core and Truss Decompositions. November 2020 http://sariyuce.com/publications.html
• Jingxin Liu, Chang Xu, Chang Yin, You Song: K-Core Based Temporal Graph Convolutional Network for Dynamic Graphs. IEEE Transactions on Knowledge and Data Engineering. October 2020 DOI: 10.1109/TKDE.2020.3033829 https://www.researchgate.net/publication/345430270_K-Core_Based_Temporal_Graph_Convolutional_Network_for_Dynamic_Graphs

Nov 2, 2021

Combine with netsJson. https://mran.microsoft.com/package/ggnetwork

For iGraph write functions read.netsJson and save.netsJson.

ZUSE Sporoćila 2021

Pri povezovanju podatkovij (npr. podatki za različna leta) ali ustvarjanju omrežij, ki vsebujejo osebne podatke, je potrebno identificirati enote (osebe). Podatki morajo biti anonimizirani. Ali je mogoče uporabiti block chain za vir anonimnih IDjev.

Nov 27, 22

p(v,C) = # of relations in the node v in the subnetwork N(C)

Algorithm, examples. Node v description (k_i) k_i = # of links from relation i.

Nov 29, 22

3D representation: over a given map in the plane we build a spatial dendrogram on units located in the map.

In a dendrogram attachment of the subtree is positioned in the ratio of number of units in the left and right subtree.

Analiza omrežja priimkov glede na različne vrste povezav

1. daljši skupni deli (začetki, konci, sredina)
2. zamenjave, izbrisi/dodatki črk (v → b → f; ć → č → tsch; q → č; s → š; x → ks; ch → h; …)

25. feb 2023

SVG viewer Podobno kot pri 3D prikazih večsmernih omrežij bi tudi pri matrikah veljalo omogočiti prikaz info o izbranem kvadratku. SVG, tooltips, zoom in/out.