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Paktor: History, Regions & Companies
Paktor is a location-based dating and networking mobile application that connects mutually interested users and allows them to chat individually or as a group. Founded in Singapore, the app was launched in June 2013, and has over 5 billion swipes to date (October 2015). In late 2013, Paktor launched a subsidiary called GaiGai, an offline dating service…
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Paktor topic overview
The analysis highlights History, Regions and Companies as prominent areas in the source structure around Paktor.
Related topics
20
Source areas
4
Connected nodes
24
Extracted relationships
78
Concept neighborhoods
15
Bridge connections
24
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
History · 10 topics
Reception · 5 topics
Overview · 3 topics
Company Overview · 2 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Developer
Paktor Pte Ltd
Operating system
iOS, Android
Release
June 2013 (2013-06)
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Paktor connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Paktor shows recurring relationship patterns in the source. For example, Paktor → Already, Asia, Bloomberg, CEO, Chinese, CNBC Asia, CNN, English, European, GDI, Global Dating Insights, In October, Jose Ruano, Meetic, Miguel Mangas, Singapore, South American, Southeast Asia, Southeast Asian, Spanish Another extracted example is Paktor → At, CEO Joseph Phua, In March, India, Malaysia, November, Philippines, Series, Series-A, Singapore, Taiwan, Thailand, The, This, US, Vertex Ventures Southeast Asia, Vietnam. Use these groups to spot repeated connection types before inspecting the individual relationships.
Paktor
Top relations
related to In the media· 25
Paktor → Already, Asia, Bloomberg, CEO, Chinese, CNBC Asia, CNN, English, European, GDI, Global Dating Insights, In October, Jose Ruano, Meetic, Miguel Mangas, Singapore, South American, Southeast Asia, Southeast Asian, Spanish
related to Financials· 17
Paktor → At, CEO Joseph Phua, In March, India, Malaysia, November, Philippines, Series, Series-A, Singapore, Taiwan, Thailand, The, This, US, Vertex Ventures Southeast Asia, Vietnam
related to Change of Management· 15
Paktor → CEO, CEO Joseph Phua, Chief Operating Officer, Colin Hodge, DOWN, In, In November, M17 Entertainment, Media, Ng Jing Shen, Paktor Group, Phua, Shn Juay, The, United States-based
related to In the region· 10
Paktor → As, Beyond Singapore, Indonesia, Malaysia, September, Southeast Asia, Taiwan, Thailand, There, Vietnam
related to Founding· 6
Paktor → Despite, Joseph Phua, June, Ng Jing Shen, Singapore, The
Developer· 1
Paktor → Paktor Pte Ltd
Operating system· 1
Paktor → iOS, Android
Release· 1
Paktor → June 2013 (2013-06)
Website· 1
Paktor → gopaktor.com
is a· 1
Paktor → location-based dating and networking mobile application that connects mutually interested users and allows them to chat individually or as a group
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 78 structured relationships around Paktor. Examples in this analysis include Paktor → Developer → Paktor Pte Ltd and Paktor → Operating system → iOS, Android. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Paktor
Developer
Paktor Pte Ltd
1.00
infobox
Paktor
Operating system
iOS, Android
1.00
infobox
Paktor
Release
June 2013 (2013-06)
1.00
infobox
Paktor
Website
gopaktor.com
1.00
infobox
Paktor
is a
location-based dating and networking mobile application that connects mutually interested users and allows them to chat individually or as a group
0.90
text
Paktor
related to Change of Management
In November
0.60
section
Paktor
related to Change of Management
Paktor Group
0.60
section
Paktor
related to Change of Management
Ng Jing Shen
0.60
section
Paktor
related to Change of Management
CEO
0.60
section
Paktor
related to Change of Management
CEO Joseph Phua
0.60
section
Paktor
related to Change of Management
Phua
0.60
section
Paktor
related to Change of Management
M17 Entertainment
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Paktor bring nearby vocabulary together. In this analysis, examples include Group, Funding and Phua. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Paktor
Group
Funding
Phua
Two
First
New
Million
June
Announced
Ceo
Social
Vietnam
paktor
Group
Funding
Phua
Two
First
New
Million
June
Announced
Ceo
Social
Vietnam
dating
Paktor
App
Mobile
Entertainment
Also
Asia
New
Announced
Ceo
Global
Next
Social
vietnam
Malaysia
Thailand
Taiwan
Indonesia
Singapore
App
Ceo
Next
Phua
Two
Also
New
m17 entertainment
Announced
New
Media
Mobile
Funding
Next
Phua
Social
Round
First
Group
Paktor
company overview
Media
Announced
Also
June
Next
Phua
Entertainment
New
Group
Paktor
Dating
Million
mobile application
Announced
Funding
Global
Entertainment
Paktor
Round
Also
Users
New
Group
Million
singapore
Malaysia
Thailand
Vietnam
Taiwan
First
Swipes
Ceo
Indonesia
Next
Phua
Users
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Paktor, one of the stronger structural bridges in this analysis connects Paktor with History. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Paktor
Nodes25
Edges24
Triples78
Avg. degree1.92
Density0.08
Components1
Source & methodology
TTTA analyzes the structure around Paktor to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Regions & Companies, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
