Network of 43 papers and 53 citation links related to "Peer Pressure and Partnerships".

We provide the instances used in the paper “Rapid Influence Maximization on Social Networks: The Positive Influence Dominating Set Problem”, by S. Raghavan and Rui Zhang, published in INFORMS Journal on Computing (https://doi.org/10.1287/ijoc.2021.1144). This repository contains the 100 instances used in the paper.

All the instances used in the paper are provided in a compressed archive. The accompanying data is contained in the following file: • PIDS_Instances.zip

Description: There is one main folder, which contains 100 instances based on 10 real-world graphs.

For graphs Gnutella, Anybeat, Advogato, Escorts, Hamster, Ning, and Delicious, the setting is as follows: For each instance file, there are m + 2 lines. The first m lines provide the edges in the graphs. Nodes are labeled from 0 to n where n is the largest number in the first m lines. The (m + 1)th line contains the weight (b) for each node. The (m + 2)th line contains the threshold value (g) for each node.

For graphs Flixster, Youtube, and Lastfm, the setting is as follows: Each real-world graph “G” is described by the file named “G_Graph.txt” which contains the edges in the graph. Nodes are labeled from 0 to n, where n is the largest number in the file. Each line provides the two end nodes of an edge. The 10 instances associated with each graph “G” are provided in the 10 files named “G_i.txt” for i in {0, 1, · · · , 9}. In each file, there are two lines. The first line contains the weight (b) for each node. The second line contains the threshold value (g) for each node.

The excel file “PIDS_Results.xlsx” reports, for each instance, the upper and lower bounds obtained in the paper.

Network of 39 papers and 79 citation links related to "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning".

Multi-stakeholder platforms (MSPs) are seen as a promising vehicle to achieve agricultural development impacts. By increasing collaboration, exchange of knowledge and influence mediation among farmers, researchers and other stakeholders, MSPs supposedly enhance their ‘capacity to innovate’ and contribute to the ‘scaling of innovations’. The objective of this paper is to explore the capacity to innovate and scaling potential of three MSPs in Burundi, Rwanda and the South Kivu province located in the eastern part of Democratic Republic of Congo (DRC). In order to do this, we apply Social Network Analysis and Exponential Random Graph Modelling (ERGM) to investigate the structural properties of the collaborative, knowledge exchange and influence networks of these MSPs and compared them against value propositions derived from the innovation network literature. Results demonstrate a number of mismatches between collaboration, knowledge exchange and influence networks for effective innovation and scaling processes in all three countries: NGOs and private sector are respectively over- and under-represented in the MSP networks. Linkages between local and higher levels are weak, and influential organisations (e.g., high-level government actors) are often not part of the MSP or are not actively linked to by other organisations. Organisations with a central position in the knowledge network are more sought out for collaboration. The scaling of innovations is primarily between the same type of organisations across different administrative levels, but not between different types of organisations. The results illustrate the potential of Social Network Analysis and ERGMs to identify the strengths and limitations of MSPs in terms of achieving development impacts.

Instagram’s most popular post

              As of April 2024, the most popular post on Instagram was Lionel Messi and his teammates after winning the 2022 FIFA World Cup with Argentina, posted by the account @leomessi. Messi's post, which racked up over 61 million likes within a day, knocked off the reigning post, which was 'Photo of an Egg'. Originally posted in January 2021, 'Photo of an Egg' surpassed the world’s most popular Instagram post at that time, which was a photo by Kylie Jenner’s daughter totaling 18 million likes.
              After several cryptic posts published by the account, World Record Egg revealed itself to be a part of a mental health campaign aimed at the pressures of social media use.

              Instagram’s most popular accounts

              As of April 2024, the official Instagram account @instagram had the most followers of any account on the platform, with 672 million followers. Portuguese footballer Cristiano Ronaldo (@cristiano) was the most followed individual with 628 million followers, while Selena Gomez (@selenagomez) was the most followed woman on the platform with 429 million. Additionally, Inter Miami CF striker Lionel Messi (@leomessi) had a total of 502 million. Celebrities such as The Rock, Kylie Jenner, and Ariana Grande all had over 380 million followers each.

              Instagram influencers

              In the United States, the leading content category of Instagram influencers was lifestyle, with 15.25 percent of influencers creating lifestyle content in 2021. Music ranked in second place with 10.96 percent, followed by family with 8.24 percent. Having a large audience can be very lucrative: Instagram influencers in the United States, Canada and the United Kingdom with over 90,000 followers made around 1,221 US dollars per post.

              Instagram around the globe

              Instagram’s worldwide popularity continues to grow, and India is the leading country in terms of number of users, with over 362.9 million users as of January 2024. The United States had 169.65 million Instagram users and Brazil had 134.6 million users. The social media platform was also very popular in Indonesia and Turkey, with 100.9 and 57.1, respectively. As of January 2024, Instagram was the fourth most popular social network in the world, behind Facebook, YouTube and WhatsApp.

Quantifying the influence of space on social group structure

Series of networks based on individual connections collected from a population of free-ranging house mice (Mus musculus domesticus). Mice are tagged with a passive integrated transponder (PIT) when they reach a minimum weight of 18 grams. The mice use 40 artificial nest boxes, fitted with radio-frequency identification (RFID) antennae, to rest and rear litters. The antennae automatically record when mice equipped with a PIT enter and exit a box. Based on these antennae data, we can determine which individuals share nest boxes and for how long. (For further information on the study system refer to König et al. 2015).

The series of networks is constructed based on the sharing of nest boxes. The series of networks consists of 14 days of antennae data over the duration of 2 years (population size during this time period ranged between 52 to 188 tagged adult house mice). Inactivity periods of the data collection system extended this time window, so that each time window consists of a similar period of active data collection (see also Liechti et al. 2020). We used total time spent sharing a nest box in seconds as our measure of association strength.

Authors:

- Julian Evans^1†*,
- Jonas I. Liechti^2†,
- Matthew J. Silk^3,4,
- Barbara König^1‡,
- Sebastian Bonhoeffer^2‡

^†: Joint first author; ^‡: Joint last author; ^*: Corresponding author: jevansbio@gmail.com

1: Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zurich
2: Institute for Integrative Biology, ETH Zurich, Universitätsstrasse 16, 8092 Zurich, Switzerland
3: Centre for Ecology and Conservation, University of Exeter Penryn Campus, UK
4: Environment and Sustainability Institute, University of Exeter Penryn Campus, UK

Dataset
The raw data of the studied system is present in `graphs` folder in form of a
list of [GraphML](http://graphml.graphdrawing.org/) files.
The files containing the graphs are numbered according to their order of
appearance in the sequence of graphs.

Each file contains a single graph the can be imported for example into
[igraph](https://igraph.org/r/doc/).

The following attributes are stored in the graphml files:

Graph properties:

- start (xml tag: g_start) gives the starting timepoint of
aggregation period. The format is `'YYYY-MM-DD HH:MM:SS'`.
- stop (xml tag: g_stop) gives the ending timepoint of the
aggregation period.

Node properties:

- name (xml tag: v_name) a unique id for each individual that is consistent
across the sequence of graphs.
- x position( xml tag: v_x) is the x position [cm] of the barycentre from
all interactions of this individual.
- y position( xml tag: v_y) is the x position [cm] of the barycentre from
all interactions of this individual.

Edge properties:

- weight (xml tag: e_weight) is the weight [seconds] of an interaction.
The weight corresponds to the accumulated duration of interactions.

This supporting document contains the following components of our analysis. (i) Formal proofs of the MGEO-P properties. (ii) Full statistical information about each of the Facebook and LinkedIn networks including the graphlet counts. (iii) Figure S1: Predicted dimensions of random graphs with the same degree distribution. (iv) Figure S2: The change in predicted dimension found by perturbing the graph structure. (v) A discussion of the sensitivity results about the predicted dimension. (PDF)

Network of 46 papers and 73 citation links related to "INTEGRATED AND DECOUPLED CORPORATE SOCIAL PERFORMANCE: MANAGEMENT COMMITMENTS, EXTERNAL PRESSURES, AND CORPORATE ETHICS PRACTICES.".

Number and (percentage) of organisations per level in the collaborative network.

Network characteristics to evaluate capacity to innovate and scaling potential of MSPs.

As of April 2024, it was found that men between the ages of 25 and 34 years made up Facebook largest audience, accounting for 18.4 percent of global users. Additionally, Facebook's second largest audience base could be found with men aged 18 to 24 years.

              Facebook connects the world

              Founded in 2004 and going public in 2012, Facebook is one of the biggest internet companies in the world with influence that goes beyond social media. It is widely considered as one of the Big Four tech companies, along with Google, Apple, and Amazon (all together known under the acronym GAFA). Facebook is the most popular social network worldwide and the company also owns three other billion-user properties: mobile messaging apps WhatsApp and Facebook Messenger,
              as well as photo-sharing app Instagram. Facebook usersThe vast majority of Facebook users connect to the social network via mobile devices. This is unsurprising, as Facebook has many users in mobile-first online markets. Currently, India ranks first in terms of Facebook audience size with 378 million users. The United States, Brazil, and Indonesia also all have more than 100 million Facebook users each.

Number and percentage of organisations per level in the knowledge networks.

Collaborative network composition and characteristics.

Characteristics of respondents (M = male, F = female).

Composition of the influence networks and MSPs.

Composition of the knowledge exchange networks.