Avraham A. Melkman

1.0k total citations
36 papers, 626 citations indexed

About

Avraham A. Melkman is a scholar working on Molecular Biology, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Avraham A. Melkman has authored 36 papers receiving a total of 626 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 10 papers in Computational Theory and Mathematics and 7 papers in Computer Networks and Communications. Recurrent topics in Avraham A. Melkman's work include Gene Regulatory Network Analysis (8 papers), Advanced Numerical Analysis Techniques (6 papers) and Bioinformatics and Genomic Networks (5 papers). Avraham A. Melkman is often cited by papers focused on Gene Regulatory Network Analysis (8 papers), Advanced Numerical Analysis Techniques (6 papers) and Bioinformatics and Genomic Networks (5 papers). Avraham A. Melkman collaborates with scholars based in Israel, Japan and United States. Avraham A. Melkman's co-authors include Charles A. Micchelli, Tatsuya Akutsu, Simon Kasif, Takeyuki Tamura, Isaac Meilijson, Alan G. Konheim, Sven Kosub, Shmuel Friedland, Stan Letovsky and Masaki Yamamoto and has published in prestigious journals such as Bioinformatics, PLoS ONE and The Annals of Statistics.

In The Last Decade

Avraham A. Melkman

33 papers receiving 555 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Avraham A. Melkman Israel 14 162 122 91 84 83 36 626
Yasuaki Kuroe Japan 15 38 0.2× 115 0.9× 81 0.9× 70 0.8× 38 0.5× 124 858
Achraf Daoui Morocco 21 43 0.3× 700 5.7× 78 0.9× 24 0.3× 60 0.7× 47 893
Allen Klinger United States 15 13 0.1× 350 2.9× 80 0.9× 18 0.2× 38 0.5× 51 698
Bernd Gärtner Switzerland 14 15 0.1× 113 0.9× 240 2.6× 18 0.2× 87 1.0× 63 692
Kenji Kashima Japan 16 82 0.5× 44 0.4× 91 1.0× 9 0.1× 64 0.8× 144 868
Bartłomiej Błaszczyszyn France 17 32 0.2× 33 0.3× 28 0.3× 142 1.7× 15 0.2× 59 1.9k
Di‐Rong Chen China 17 51 0.3× 327 2.7× 54 0.6× 130 1.5× 444 5.3× 79 961
Gábor Pataki United States 11 18 0.1× 48 0.4× 216 2.4× 21 0.3× 151 1.8× 19 515
Raif M. Rustamov United States 14 136 0.8× 552 4.5× 102 1.1× 24 0.3× 635 7.7× 22 1.1k
Der-Tsai Lee Taiwan 10 23 0.1× 158 1.3× 59 0.6× 13 0.2× 42 0.5× 17 1.1k

Countries citing papers authored by Avraham A. Melkman

Since Specialization
Citations

This map shows the geographic impact of Avraham A. Melkman's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Avraham A. Melkman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Avraham A. Melkman more than expected).

Fields of papers citing papers by Avraham A. Melkman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Avraham A. Melkman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Avraham A. Melkman. The network helps show where Avraham A. Melkman may publish in the future.

Co-authorship network of co-authors of Avraham A. Melkman

This figure shows the co-authorship network connecting the top 25 collaborators of Avraham A. Melkman. A scholar is included among the top collaborators of Avraham A. Melkman based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Avraham A. Melkman. Avraham A. Melkman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Akutsu, Tatsuya & Avraham A. Melkman. (2023). On the Size and Width of the Decoder of a Boolean Threshold Autoencoder. IEEE Transactions on Neural Networks and Learning Systems. 36(2). 3855–3862. 1 indexed citations
2.
Melkman, Avraham A., et al.. (2020). Extracting boolean and probabilistic rules from trained neural networks. Neural Networks. 126. 300–311. 2 indexed citations
3.
Akutsu, Tatsuya, Avraham A. Melkman, & Takeyuki Tamura. (2020). Improved Hardness of Maximum Common Subgraph Problems on Labeled Graphs of Bounded Treewidth and Bounded Degree. International Journal of Foundations of Computer Science. 31(2). 253–273.
4.
Akutsu, Tatsuya & Avraham A. Melkman. (2018). Identification of the Structure of a Probabilistic Boolean Network From Samples Including Frequencies of Outcomes. IEEE Transactions on Neural Networks and Learning Systems. 30(8). 2383–2396. 13 indexed citations
5.
Melkman, Avraham A., Xiaoqing Cheng, Wai‐Ki Ching, & Tatsuya Akutsu. (2017). Identifying a Probabilistic Boolean Threshold Network From Samples. IEEE Transactions on Neural Networks and Learning Systems. 29(4). 869–881. 16 indexed citations
6.
Akutsu, Tatsuya, Takeyuki Tamura, Avraham A. Melkman, & Atsuhiro Takasu. (2014). On the complexity of finding a largest common subtree of bounded degree. Theoretical Computer Science. 590. 2–16. 3 indexed citations
7.
Melkman, Avraham A. & Tatsuya Akutsu. (2013). An Improved Satisfiability Algorithm for Nested Canalyzing Functions and its Application to Determining a Singleton Attractor of a Boolean Network. Journal of Computational Biology. 20(12). 958–969. 8 indexed citations
8.
Akutsu, Tatsuya, Sven Kosub, Avraham A. Melkman, & Takeyuki Tamura. (2012). Finding a Periodic Attractor of a Boolean Network. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(5). 1410–1421. 32 indexed citations
9.
Akutsu, Tatsuya, Avraham A. Melkman, Takeyuki Tamura, & Masaki Yamamoto. (2011). Determining a Singleton Attractor of a Boolean Network with Nested Canalyzing Functions. Journal of Computational Biology. 18(10). 1275–1290. 17 indexed citations
10.
Akutsu, Tatsuya, Avraham A. Melkman, & Takeyuki Tamura. (2011). Singleton and 2-periodic attractors of sign-definite Boolean networks. Information Processing Letters. 112(1-2). 35–38. 4 indexed citations
11.
Dolev, Shlomi, et al.. (2002). Smooth and adaptive forward erasure correcting. 483–486.
12.
Dolev, Shlomi, et al.. (2001). Smooth and adaptive forward erasure correcting. Computer Networks. 36(2-3). 343–355. 6 indexed citations
13.
Melkman, Avraham A.. (1996). Another Proof of the Total Positivity of the Discrete Spline Collocation Matrix. Journal of Approximation Theory. 84(3). 265–273. 3 indexed citations
14.
Leiser, David, Yoella Bereby‐Meyer, & Avraham A. Melkman. (1995). A comparison of display methods for spatial point layout. Behaviour and Information Technology. 14(3). 135–142. 1 indexed citations
15.
Melkman, Avraham A.. (1984). The distance of a subspace of R from its axes and n-widths of octahedra. Journal of Approximation Theory. 42(3). 245–256. 2 indexed citations
16.
Friedland, Shmuel & Avraham A. Melkman. (1979). On the eigenvalues of non-negative Jacobi matrices. Linear Algebra and its Applications. 25. 239–253. 15 indexed citations
17.
Melkman, Avraham A. & Charles A. Micchelli. (1978). Spline spaces are optimal for $L^{2}\,\,n$-width. Illinois Journal of Mathematics. 22(4). 20 indexed citations
18.
Melkman, Avraham A.. (1977). Splines with maximal zero sets. Journal of Mathematical Analysis and Applications. 61(3). 739–751. 3 indexed citations
19.
Melkman, Avraham A.. (1977). Hermite-Birkhoff interpolation by splines. Journal of Approximation Theory. 19(3). 259–279. 8 indexed citations
20.
Melkman, Avraham A.. (1974). The Budan-Fourier theorem for splines. Israel Journal of Mathematics. 19(3). 256–263. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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