The document analyzes actor networks involved in climate change adaptation in Bhutan. It finds that while national actors are relatively well embedded in top-down networks, both top-down and bottom-up networks suffer from fragmentation. The analysis also finds evidence of clustering by actor type, with national actors forming the core network. The document recommends a hybrid "polycentric" governance approach to increase local actor inclusion, vertical and horizontal collaboration, and reduce fragmentation between actor networks.
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Academics establish relationships by way of various interactions like jointly authoring a research paper or report, jointly
supervising a thesis, working jointly on a project, etc. Some of these relationships are ubiquitous whereas other are hard to keep track
of. Of all types of possible academic and research collaborations, co-authorship is best documented. In this paper we analyze the coauthorship
based academic social networks of computer science engineering departments of Indian Institutes of Technology (IITs) as
evidenced from their research publications produced during 2011 and 2015. We use social network analysis metrics to study the
collaboration networks in four leading IITs. From experimental results it can be concluded that IIT Delhi and IIT Kharagpur have a
close knit collaboration network whereas the collaboration network of IIT Kanpur and IIT Madras is fragmented. However, the
collaboration networks of all the four IITs exhibit similar network properties as expected from any other collaboration network
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ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
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This result shows that winter is becoming dryer and rainy season is getting more and more rain that signifies essential need of reservoir based hydro powers also with greater water holding capacity in its reservoir. Similarly, there is temporal variation of different climate characteristics such as amount and intensity of rainfall, temperature and discharge in the river in study area. With the change in precepetatin pattern, Kulekhani in monsoon is receiving more rainfall on lesser number of days, this shows the chances of more sediment production in the watershed that lead to shorten lifespan of the reservoir.
Social Network Analysis for Competitive IntelligenceAugust Jackson
How can CI teams apply the concepts of social network analysis to gain insight into the capabilities and plans of their competitors? Presented by Jim Richardson and August Jackson in April 2007 at the Society of Competitive Intelligence Professionals annual conference in New York City.
Network and spatial analysis for forest governanceCIFOR-ICRAF
Presented by Dr. Matt Hamilton, The Ohio State University, USA and Dr. Caleb Gallemore, Lafayette College, USA, on 10 November 2020 at "International workshop: Enhancing wetland management and sustainable development"
Mining and Analyzing Academic Social NetworksEditor IJCATR
Academics establish relationships by way of various interactions like jointly authoring a research paper or report, jointly
supervising a thesis, working jointly on a project, etc. Some of these relationships are ubiquitous whereas other are hard to keep track
of. Of all types of possible academic and research collaborations, co-authorship is best documented. In this paper we analyze the coauthorship
based academic social networks of computer science engineering departments of Indian Institutes of Technology (IITs) as
evidenced from their research publications produced during 2011 and 2015. We use social network analysis metrics to study the
collaboration networks in four leading IITs. From experimental results it can be concluded that IIT Delhi and IIT Kharagpur have a
close knit collaboration network whereas the collaboration network of IIT Kanpur and IIT Madras is fragmented. However, the
collaboration networks of all the four IITs exhibit similar network properties as expected from any other collaboration network
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...BAINIDA
Subscriber Churn Prediction Model using Social Network Analysis In Telecommunication Industry โดย เชษฐพงศ์ ปัญญาชนกุล อาจารย์ ดร. อานนท์ ศักดิ์วรวิชญ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Climate Change Impacts on Reservoir based Hydropower Generation in Nepal: A c...Manjeet Dhakal
This result shows that winter is becoming dryer and rainy season is getting more and more rain that signifies essential need of reservoir based hydro powers also with greater water holding capacity in its reservoir. Similarly, there is temporal variation of different climate characteristics such as amount and intensity of rainfall, temperature and discharge in the river in study area. With the change in precepetatin pattern, Kulekhani in monsoon is receiving more rainfall on lesser number of days, this shows the chances of more sediment production in the watershed that lead to shorten lifespan of the reservoir.
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Social network analysis plays an important role in analyzing social relations and patterns of interaction among actors in a
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quantitative features to numerically define various attributes of the network. These features also referred to as social network metrics
used everyday mathematics as their foundations. In this paper we provide an overview of various social network analysis metrics that
are commonly used to analyse social networks. Explanation of these metrics and their relevance for academic social networks is also
outlined
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The social network analysis (SNA), branch of complex systems can be used in the construction of multiagent
systems. This paper proposes a study of how social network analysis can assist in modeling multiagent
systems, while addressing similarities and differences between the two theories. We built a prototype
of multi-agent systems for resolution of tasks through the formation of teams of agents that are formed on
the basis of the social network established between agents. Agents make use of performance indicators to
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9 A Preliminary Theory of Interorganizational Network Effectivenes.docxransayo
9 A Preliminary Theory of Interorganizational Network Effectiveness: A Comparative Study of Four Community Mental Health Systems Keith G. Provan H. Brinton Milward This chapter presents the results of a comparative study of interorganizational networks, or systems, of mental health delivery in four U.S. cities, leading to a preliminary theory of network effectiveness. Extensive data were collected from surveys, interviews, documents, and observations. Network effectiveness was assessed by collecting and aggregating data on outcomes from samples of clients, their families, and their case managers at each site. Results of analyses of both quantitative and qualitative data collected at the individual, organizational, and network levels of analysis showed that network effectiveness could be explained by various structural and contextual factors, specifically, network integration, external control, system stability, and environmental resource munificence. Based on the findings, we develop testable propositions to guide theory development and future research on network effectiveness. The study of relations between organizations has been a major concern of organization theorists for at least the past 25 years. While most of the work in this area has focused on the determinants or predictors of interorganizational relations (see Oliver, 1990, for a review), as an understanding of the phenomenon has grown, the unit of analysis has gradually shifted from the dyad to the organization set, to the network. Especially in recent years, the study of organizational networks has proliferated. Much of this interest has been generated by an emerging recognition by academics that businesses, as well as organizations in the not-for-profit and public sectors, are increasingly turning to various forms of cooperative alliances as a way of enhancing competitiveness and effectiveness that would not be possible through the traditional governance mechanisms of market or hierarchy (Powell, 1990). While a good deal of what has been written about networks has been atheoretical, discussing the advantages of networks or examining issues of measurement and analysis, considerable theory-based research has also emerged (e.g., Cook, 1977; Burt, 1980; Granovetter, 1985; Jarillo, 1988; Williamson, 1991; Cook and Whitmeyer, 1992; Larson, 1992; Provan, 1993). In the organization theory literature, work on networks has been guided primarily by two theoretical perspectives: resource dependence, and related exchange perspectives, and transaction cost economics, with most recent work focusing on the latter approach. Each of these perspectives offers both complementary and contrasting views about the network form. For the most part, however, each perspective focuses essentially on the organizational antecedents and outcomes of network involvement, with little attention paid to the network as a whole, except for its governance and structure. This organizational view is understandable, sinc.
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Abstract: The online social network has become popular for sharing the information. Online social networks exhibit many platforms to create awareness of new products. In recent time Least cost Influence problem to find minimum number of seed user is most important topic in online social networks. The eventual target is to find the least advertising cost set of users which produce enormous influence. In existing many diffusion models are used. In this paper the stochastic threshold model is used to find the seed user in multiple online social networks to maximize the influence. This model decreases the processing time comparing to the other models.
Keywords: Stochastic threshold model, influence, diffusion, multiple networks, online social networks.
Title: Least Cost Influence by Mapping Online Social Networks
Author: Bessmitha S, Shajini N
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
Information, Knowledge Management & Coordination Systems: Complex Systems App...CITE
Date: 4 Jun 2013
Time: 12:45pm - 2:00pm
Venue: Room 101, Runme Shaw Building, The University of Hong Kong
Speakers: Professor Liaquat Hossain, University of Sydney
------------------------------------
http://www.cite.hku.hk/news.php?id=502&category=conference
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Based on a transactional dataset of almost 10,000 interactions with an online community of 32 postgraduate students who were following the same online course, we find evidence that power users also exist in the context of online learning. However, whilst they do create a lot of content, we find that they are not fundamental to keeping the group together, and in fact are less adept at creating content which generates responses than other “normal” users. This suggests that online learning communities may have different dynamics to other types of electronic community: it also suggests that design efforts should not be focused solely on attracting a small core of “power learners”. Rather, diverse types of users are needed for online learning communities to survive and prosper.
Authors:
Cristóbal Cobo, Center for Research - Ceibal Foundation, Uruguay
Monica Bulger, Data & Society Research Institute, United States
Jonathan Bright, Oxford Internet Institute, United Kingdom
Ryan den Rooijen, Oxford Internet Institute, United Kingdom
Presented at the LINC Conference (MIT, 2016) Digital Inclusion: Transforming Education through Technology.
Hamalt genetics based peer to-peer network architecture to encourage the coo...csandit
Since its inception, Internet has grown tremendously not only in the size of its customers but
also with the technology used behind to run it. For the well ex-istence and proper development
of Peer-to-Peer Networks, all nodes in the overlay must be cooperative and donate their
resources for any other peer. The paper dis-cusses the reason of peers being selfish, causes of
selfish peers and the methods used so far to resolve selfish peers problem. A Genetic Algorithm
based solution has been proposed in this paper that solves the selfish nodes problem in Peer-to-
Peer Networks and that also encourages the cooperation among all nodes in the overlay. An
architecture HAMALT is proposed in this paper for disseminating altruism among the peers.
Hamalt genetics based peer to-peer network architecture to encourage the coo...
Master thesis presentation
1. Complex climate change adaptation network in Bhutan;
how actor type impacts actor inclusion and clustering
Master’s Thesis
Institute of Political Science
University of Bern, Switzerland
Presented by:
Jessica Russell
17 September 2015
Thesis supervisor:
Prof. Dr. Karin Ingold
Institute of Political Science
University of Bern, Switzerland
2. Background
> Policy processes and governance as networks:
Actors participate in collective decision-making process’ called an “institution”
Repeated transactions between actors become institutionalized in a “network”
Actors collaborate within the network on a defined project, creating a “tie”
Stakeholder inclusion, scales and multi-level approaches:
Climate change adaptation is local in scope, with the structure of institutional
arrangements being critical, local actor inclusion is important
The multilevel governance framework involves two levels of action:
1) Vertical = crossing multiple government scales - local to national
2) Horizontal = across government departments, line ministries
Vertical/horizontal integration through top-down/bottom-up institutional designs
A “polycentric approach” to governance
Case study: Bhutan
3. Research Question 1
> Aim: Examine how actor inclusion in climate change adaptation institutions is
shaped in a complex network in Bhutan, by applying a multilevel governance
framework to explore links between actors and institutions within networks
> Research Question: Which type of actors are most included within complex
networks and projects in climate change adaptation policy in Bhutan via the
embededness of national and local actors in top-down and bottom-up institutional
designs
> Hypothesis: National actors are well embedded in top-down institutional designs,
and local actors are well embedded in bottom-up institutional designs
Independent Variable: Actor type
Dependent Variable: Actors’ inclusion
4. Data and Methodology – Research
Questions 1, 2, 3
> 130 key actors were identified from a literature review and defined as nodes,
categorised into 7 actor types: international organisation, national government,
foreign government, local government, NGO, corporation or community
> 73 climate change adaptation-related policies/projects defined and categorised
into:
Institutional types: climate change adaptation, sustainable forestry, disaster
management, communication (14)
Institutional designs: top-down (69) or bottom-up (4)
Social Network Analysis (SNA). Software- UCINET:
Ties between actors and institutions coded as a dummy variable, with 1: tie
being present, and 0: no tie present
Binary ties make up the 2 mode (actor x institution) asymmetric network matrix
2 mode data transformed into a 1 mode (actor x actor) symmetric network
matrix to run analysis
Ties are undirected
5. Research Question 1: Methodology
Networks Analysed:
Total network: All actors as nodes, with ties (both top-down and bottom-up),
in a transformed 2 mode to 1 mode network
Sub-networks: Either top-down or bottom-up institutional designs, are also
made up of actors as nodes, with ties, in a transformed 2 mode to 1 mode
network
> Method- Research Question 1
Social Network Analysis (SNA): three measures of centrality to assess
embededness:
Degree centrality: the number of ties an actor directly shares with others in
the network
Betweeness centrality: the number of times an actor connects two
disconnected actors
Eigenvector centrality: higher when an actor is connected to institutions that
are also well connected
6. Research Question 1: Methodology
> Analysis:
T-test: compare the mean centrality: degree, betweeness and eigenvector of
national government actors and all other groups (total network and top-down
designs), and local government actors and all other groups (bottom-up
designs)
Two-way ANOVA: tests the level of significance of differences in normed
centrality: degree, betweeness, and eigenvector means between the 7 actor
types, with the p-value
Regression model: to fit the data and estimate the significance and strength
of the relationship between independent variable actor type and dependent
variable actors’ inclusion (dependent variable: centrality)
If an actor has a high degree centrality, betweeness centrality and eigenvector
centrality, relative to others = actor is well embedded in the network
7. Table 3. Normed betweeness centrality means for the total network and sub-networks
8. Research Question 1: Results
> Hypothesis: National actors are well embedded in top-down institutional designs,
and local actors are well embedded in bottom-up institutional designs
National governments do not have a high degree centrality (Table 2- Annex),
but high betweeness centrality (Table 3) and eigenvector centrality (Table 4-
Annex) in top-down designs
Local actors have a slightly higher degree centrality, no betweeness centrality,
but a high eigenvector centrality, in bottom-up institutional designs
No significant effect of actor type on degree, betweeness, eigenvector
centrality
Conclude: National actors are relatively better embedded in top-down
institutional designs, and local actors are relatively better embedded in
bottom-up institutional designs
9. The Relevance of Clustering
Research Question 2: Part A and B
> Clusters of actors collaborate inside closed groups, having repeated transactions
leading to trust, thus supporting cooperation
> Core/Periphery- Actors in the core are more able to coordinate their activities
> Aim: Examine whether clustering occurs according to actor type in climate change
adaptation institutions in a complex network in Bhutan, by applying a multilevel
governance framework to explore links between actors and institutions
> Research Question 2. Part A: Do actors of the same type cluster together within
networks?
Hypothesis: Actors of the same type collaborate by clustering; the density
between within-group ties and outside-group ties are significantly different
Independent Variable: Actor type
Dependent Variable: Clustering
> Research Question 2. Part B: Which actors make up the core and periphery of
the network?
10. Research Question 2: Part A and B
Methodology
> PART A & B: An MDS plot is used to initially locate clusters:
Actors located in the centre of the cluster = core actors
Actors located further away from the centre = peripheral actors
Analysis: Total network (both top-down and bottom-up institutional designs)
> Part A: Clustering analysed via tie density:
Structural Block model option of an ANOVA Density model: test whether within
and between group ties are significantly different across actor types
> Part B: Location via a simple Core/Periphery model:
Core actors = highest density of ties amongst themselves, collaborating in
common institutions
Peripheral actors = lower density of ties amongst themselves, fewer
institutions in common
11. Figure 5. An MDS plot shows the total network.
Red circles: international organisations, orange: foreign government, yellow: national
government, green: local government, blue: NGO, purple: corporation, pink: community
12. Table 5. Structural Block model option of an ANOVA Density model (a) with model fit (b), and a
Simple Core/Periphery model with density matrix table (c) for the total network
13. Research Question 2: Results
> Part A: Do actors of the same type cluster together within networks?
Hypothesis: Actors of the same type collaborate by clustering; the density
between within-group ties and outside-group ties are significantly different
Results: There is a statistically significant effect of variable “actor type” on
variable “clustering” in the network; accept that the density between within-
group ties and outside-group ties are significantly different
> Part B: Which actors make up the core and periphery of the network?
Results: Many national level actors in the core
> Conclude:
Actors of the same type collaborate by clustering
National actors cluster and occupy the core of the network, sharing many ties
and cooperating with other actor types
14. The Relevance of Fragmentation
Research Question 3
> We define fragmentation where there is a high proportion of actors unable to reach
each-other in the network:
Vertical fragmentation: Between institutions at different scales such as from
national to local
Horizontal fragmentation: Between institutions in different sectors such as
forestry or water
> Fragmentation in institutions:
Could affect actors’ ability to implement projects effectively, through inhibiting
joint decision-making
Creates barriers to cooperation
> Aim: Examine fragmentation in climate change adaptation institutions in a complex
network in Bhutan, by applying a multilevel governance framework to explore links
between actors and institutions within networks
> Research Question: Does the network display fragmentation?
15. Research Question 3 - Hypothesis and
Methodology
> Hypothesis: Top-down institutional designs are less fragmented than bottom-up
institutional designs
> Fragmentation analysis:
Networks: the total network (both top-down and bottom-up institutional
designs), and sub-networks; top-down and bottom-up
Fragmentation score: the proportion of actors unable to reach each-other
Compare the fragmentation score of institutional design relative to each-other
> Density score:
Equal to 1 = all actors within the network are tied directly to each other
Equal to 0 = network is fully disconnected and therefore fragmented
16. Research Question 3: Results
Table 6. Fragmentation score for the total network, and sub-networks; top-down or bottom-up
institutional designs
> Results:
Low amount of fragmentation in top-down designs
High amount of fragmentation in bottom-up designs
Overall density scores (Table 1- Annex) are low for each institutional design
(top-down = 0.035, bottom-up = 0.052)
Conclude: Few ties through which information can flow, both networks are
disconnected and therefore fragmented
17. Conclusion
Some difference between top-down and bottom-up institutional designs:
National actors relatively better embedded in top-down, local actors relatively
better embedded in bottom-up (Table 2- Annex, Table 3, Table 4- Annex)
Lack of inclusion of local actors (Table 2, 3, 4)
Some actors may work on climate change projects in isolation- UNFAO (Fig.
5)
Actors of the same type collaborate by clustering (Figure 5) (Table 5)
National actors cluster/core of the network, sharing many ties/cooperate with
different actor types (Table 5)
Top-down institutional designs less fragmented than bottom-up, but with
overall low density scores for each (Table 1-Annex). Conclude that both
demonstrate fragmentation (Table 1- Annex) (Table 6)
The total network in Bhutan is fragmented (Table 1- Annex) (Table 6), but
displays clustering (Table 5); conclude that information sharing between actors
to plan, monitor and enforce climate change adaptation projects may be low
18. Recommendations
> General recommendations:
Increased involvement from national government actors: more influence, ability
to collaborate and capacity to coordinate in the network
> Recommendations for a “polycentric” approach to governance:
A hybrid approach between top-down, and bottom-up designs, with national
government providing a guiding framework, allowing local communities to
make implementation decisions based on community knowledge
Increase both horizontal and especially vertical collaboration, to increase local
actor inclusion
Support vertical and horizontal integration, to decrease fragmentation