AMiner Dataset
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SN |
Name |
Node |
Edge |
Description |
|---|---|---|---|---|
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1 |
1,572,277 papers |
2,084,019 citation relationships |
Citation network |
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|
2 |
4,794 authors |
2,164 advisor-advisee, 3,932 coauthor relationships |
Advisor-advisee network |
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3 |
640,134 authors of 8 topics |
1,554,643 coauthor relationships |
Topic based Coauthor network |
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4 |
33,739 authors of 5 topics |
139,278 coauthor relationships |
Created for cross domain recommendation |
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5 |
2,329,760 papers |
12,710,347 citation relationships |
Topic based citation network |
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6 |
8,000 papers of 27 conferences |
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Created for community detection |
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7 |
1,629,217 authors |
2,623,832 coauthor relationships |
An evolving coauthor network with 27 time stamps |
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8 |
898 files |
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Created for researcher profile extraction |
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9 |
155 citation pairs |
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Created to study the semantics of the citation relationships |
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10 |
1,781 experts of 13 topics |
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A benchmark for expert finding |
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11 |
8,369 author pairs of 9 topics |
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Created for association search |
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12 |
Top 1,000,000 papers and authors of 200 topics |
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The results of ACT model on AMiner dataset |
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13 |
1,560,640 authors |
4,258,946 coauthor relationships |
Coauthor network |
Citation [Download]
The data set is designed for research purpose only. The citation data is extracted from DBLP, ACM, 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.
A larger version will be released soon.
Advisor-advisee [Download]
This data set contains 6 different networks: Epinions, Slashdot, MobileU, MobileD, Coauthor, and Enron.
- Epinions is a network of product reviewers. Each user on the site can post a review for any product and other users would rate the review with trust or distrust. The data set consists of 131,828 users and 841,372 relationships, of which about 85.0% are trust relationships. 80,668 users received at least one trust or distrust relationships.
- Slashdot is a network of friends. Slashdot is a site for sharing technology related news. The data set is comprised of 77,357 users and 516,575 relationships of which 76.7% are ``friend'' relationships.
- MobileU is a network of mobile users. It consists of the logs of calls, blue-tooth scanning data and cell tower IDs of 107 users during about ten months. In total, the data contains 5,436 relationships.
- MobileD is a relatively larger mobile network of enterprise, where nodes are employees in a company and relationships are formed by calls and short messages sent between each other during a few months. In total, there are 232 users (50 managers and 182 ordinary employees) and 3,567 relationships (including calling and texting messages) between the users.
- Coauthor is a network of authors. The data set, crawled from Arnetminer.org, is comprised of 815,946 authors and 2,792,833 coauthor relationships.
- Enron is an email communication network. It consists of 136,329 emails between 151 Enron employees. Two types of relationships, i.e., manager-subordinate and colleague, were annotated between these employees. There are in total 3,572 relationships, of which 133 are manager-subordinate relationships.
Topic-coauthor [Download]
Co-author set consists of authors and coauthor relationship chosen from ArnetMiner. The dataset consists of 8 topics:
Topic 16: Data Mining / Association Rules
Topic 107: Web Services
Topic 131: Bayesian Networks / Belief function
Topic 144: Web Mining / Information Fusion
Topic 145: Semantic Web / Description Logics
Topic 162: Machine Learning
Topic 24: Database Systems / XML Data
Topic 75: Information Retrieval.
Topic-paper-author [Download]
The dataset is collected for the purpose of cross domain recommendation.
- Data Mining: We use papers of the following data mining conferences: KDD, SDM, ICDM, WSDM and PKDD as ground truth, which result in a network with 6,282 authors and 22,862 co-author relationships.
- Medical Informatics: We include the following journals: Journal of the American Medical Informatics Association, Journal of Biomedical Informatics, and Artificial Intelligence in Medicine, IEEE Trans. Med. Imaging and IEEE Transactions on Information and Technology in Biomedicine, from which we obtain a network of 9,150 authors and 31,851 coauthor relationships.
- Theory: We include the following conferences, i.e., STOC, FOCS and SODA, from which we get 5,449 authors and 27,712 co-author relationships.
- Visualization: We include the following conferences and journals, CVPR, ICCV, VAST, TVCG, IEEE Visualization and Information Visualization. The obtained coauthor network is comprised of 5,268 authors and 19,261 co-author relationships.
- Database: We include the following conferences, i.e., SIGMOD, VLDB and ICDE. From those conferences, we extract 7,590 authors and 37,592 co-author relationships.
Topic-citation [Download]
Citation network consists of paper and citation relationship chosen from ArnetMiner. The raw citation data consists of 2555 papers and 6101 citation relationship. The papers are mainly from 10 research fields:
Topic 16: Data Mining / Association Rules
Topic 107: Web Services
Topic 131: Bayesian Networks / Belief function
Topic 144: Web Mining / Information Fusion
Topic 145: Semantic Web / Description Logics
Topic 162: Machine Learning
Topic 24: Database Systems / XML Data
Topic 75: Information Retrieval
Topic 182: Pattern recognition / Image analysis
Topic 199: Natural Language System / Statistical Machine Translation.
Kernel community [Download]
This data set includes three different real-world social networks:
- Coauthor (a co-authorship network with 822,415 nodes and 2,928,360 undirected edges). Each vertex represents an author and each edge represents a co-author relation.
- Wikipedia (a co-editorship network with 310,990 nodes and 10,780,996 undirected edges crawled from Wikipedia.org). Each vertex represents a Wikipedia editor and each edge represents a co-editing relation.
- Twitter (a following network with 465,023 nodes and 833,590 directed edges crawled from twitter.com). Each vertex represents a Twitter user account and each edge represents a following relation. It is well-known that the web displays a bow-tie structure [20], where 30% of the vertices are strongly connected. We conduct a bow-tie analysis on the Twitter network, and discover that only 8% (38,913) of the vertices are strongly connected.
Dynamic coauthor [Download]
We construct the evolving coauthor network from ArnetMiner5. We collected 1,768,776 publications published during 1986 to 2012 with 1,629,217 authors involved. We regard each year as a time stamp and there are 27 time stamps in total. At each time stamp, we create an edge between two authors if they have coauthored at least one paper in the most recent 3 years (including the current year). We convert the undirected coauthor network into directed network by regarding each undirected edge as two symmetric directed edges.
Research profiling [Download]
We are developing extraction tools in ArnetMiner, a researcher social network system. The tool will be used to extract researcher profile from the Web page and outputs the extracted information into a researcher database.
The data set and related documents are used for researcher profile extraction.
Citation link annotation [Download]
The work intends to study how to quantify link semantics. Specifically, an ideal output of link semantics analysis is to provide users with the following information: (1) multiple topics discussed in each page; (2) semantics of a link between two pages; and (3) the influential strength of each link. With such an analysis, a user could easily trace the origins of an idea/technique, analyze the evolution and impact of a topic, filter the pages by certain categories of links, as well as zoom in and zoom out the linkage tracing graph with the degree of influence.
This data set consists of publication papers chosen from ArnetMiner. original_data.rar contains both original papers, some contains the whole content, others only contain the abstract, and annotate_data.txt is the output of the annotation tool.
Expert Finding [Download]
We have collected topics and their related people lists from as many sources as possible. We randomly chose 13 topics and created 13 people lists. The data sets were used as the “golden metric” for expert finding. They were also used to create the test sets for association search. The following table shows the 13 topics and statistics of people we have collected. In the 13 topics, OA and SW are from PC members of the related conferences or workshops. DM is from a list of data mining people organized by kmining.com. IE is from a list of information extraction researchers that were collected by Muslea. BS and SVM are from their official web sites, respectively. PL, IA, ML, and NLP are from a page organized by Russell and Norvig, which links to 849 pages around the web with information on Artificial Intelligence.
Association Search [Download]
To evaluate the effectiveness of our proposed association search approach, we created 8 test sets. Each of the person pair contains a source person (including his name and id) and a target person (including his name and id). The test sets were created as follows. We randomly selected 1,000 person pairs from the researcher network and create the first test set.
We use the above people lists to create the other 8 test sets. We created four test sets by randomly selecting person pairs from SW, DM, and IE respectively. With the three test sets, we are aimed at testing association search between persons from the same research community. We created the other five test sets by selecting persons from different research fields.
Topic model results for Arnetminer dataset [Download]
- Aminer Author Name and ID
- It consists mapping between name and id of authors in Arnetminer. The data is form as a 2 column list. The first column is Arnetminer id and the second column is Author name.
- Aminer Topic Top 5000 Publications and Authors
- It consists the top 5000 publication of each topics in Arnetminer. The data is formed as 3 xml files. Each consists data of topics, publications and authors respectively.
- ACTMaps Author Topic
- It consists the topic distribution given author. The data is organized into 733602 rows, each for an author. For each row, it consists columns separated by a blank space. Each column is the topic id and weight separated by a ":"
- Aminer FOAF Data Set
- It consists of the FOAF data of authors in www.aminer.cn. The data is organized in standard FOAF format.
Coauthor [Download]
This data set contains 5 files:
- AMiner-Author.zip
- This file saves the author information.
- AMinerCoauthor.graph.zip
- This file saves the collaboration network among the authors in the first file.
- AMinerCoauthor.dict.zip
- This file saves the mapping from the index in the first file to the ID in the second file.
- AMinerCoauthor.panther.zip
- This file saves the top-50 similar authors and the corresponding similarities for each author in the above AMiner coauthor network output by the method Panther. The line number denotes the ID of the author whose similar authors are presented.
- AMinerCoauthor.panther++.zip
- This file saves the top-50 similar authors and the corresponding Euclidean distances for each author in the above AMiner coauthor network output by the method Panther++. The line number denotes the ID of the author whose similar authors are presented.