Takahiro Hara

7.4k citations
409 papers · 4.9k indexed · 2 hit papers · h-index 33

Takahiro Hara

383 papers receiving 4.6k citations

Hit Papers

A Multi-Objective Optimization Scheduling Method Based on...2632011202620162021100200300

Peers

Takahiro Hara
Comparison fields: 5 of 152
  • Computer Networks and Communications 2.7k
  • Signal Processing 372
  • Information Systems 700
  • Transportation 203
  • Artificial Intelligence 777
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Citations per year

Countries citing papers authored by Takahiro Hara

Since Specialization
Citations

This map shows the geographic impact of Takahiro Hara'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 Takahiro Hara with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takahiro Hara more than expected).

Fields of papers citing papers by Takahiro Hara

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Takahiro Hara. 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 Takahiro Hara. The network helps show where Takahiro Hara may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Takahiro Hara, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Takahiro Hara Line = papers co-authored together Takahiro Hara links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20240
2 20232
3 20234
4 20216
5 20217
6
Discovering Social Media Timing Signals for Predicting Temporary Deal Success
20204
7 20201
8 20196
9 201821
10 20142
11 201315
12
Integration of traffic reduction and sleep scheduling for energy-efficient data gathering in wireless sensor networks.
20133
13
A Data Aggregation System using Mobile Agents on Integrated Sensor Networks
20112
14
Efficient Top-k Query Processing in Mobile Ad Hoc Networks
20102
15
An Automatic Scrolling Method for Web Page Browsing on Small Screen Devices
20052
16
GDS (Global Delayed Session) Music ‒ new improvisational music with network latency
20032
17
Replicating Data with Aperiodic Update in Ad Hoc Networks.
20023
18
A Collaborative Query Processing Method for a Database Broadcasting System.
20023
19
Replica Allocation for Improving Data Availability in Ad Hoc Networks
20011
20
On a Broadcast Data Interval of Correlative Data
19991

About Takahiro Hara

Takahiro Hara is a scholar working on Computer Networks and Communications, Signal Processing and Information Systems, having authored 409 papers that have together received 4.9k indexed citations. Recurring topics across this work include Opportunistic and Delay-Tolerant Networks (99 papers), Mobile Ad Hoc Networks (93 papers), Energy Efficient Wireless Sensor Networks (83 papers), Caching and Content Delivery (58 papers), Data Management and Algorithms (42 papers), Recommender Systems and Techniques (33 papers), Web Data Mining and Analysis (33 papers) and Peer-to-Peer Network Technologies (32 papers). The work is most often cited by research in Computer Networks and Communications (2.7k citations), Signal Processing (372 citations) and Information Systems (700 citations). Takahiro Hara has collaborated with scholars based in Japan, China and United States. Frequent co-authors include Shojiro Nishio, Lei Shu, Sanjay Madria, Daichi Amagata, Guangjie Han, Chunsheng Zhu, Huihui Xu, Jinfang Jiang, Trung Q. Duong and Takuya Maekawa. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and Proceedings of the IEEE.

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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