# Neighbor-Neighbor Correlations Explain Measurement Bias in Networks

@article{Wu2017NeighborNeighborCE, title={Neighbor-Neighbor Correlations Explain Measurement Bias in Networks}, author={Xin-Zeng Wu and Allon G. Percus and Kristina Lerman}, journal={Scientific Reports}, year={2017}, volume={7} }

In numerous physical models on networks, dynamics are based on interactions that exclusively involve properties of a node’s nearest neighbors. [... ] Key Method We develop a model to predict the magnitude of the paradox, showing that it is enhanced by negative correlations between degrees of neighboring nodes. We then show that by including neighbor-neighbor correlations, which are degree correlations one step beyond those of neighboring nodes, we accurately predict the impact of the strong friendship paradox in… Expand

## 10 Citations

### Impact of perception models on friendship paradox and opinion formation.

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It is found that it takes the longest time to reach consensus when individuals adopt the median-based perception model compared to other versions, suggesting that one needs to consider the proper perception model for better modeling human behaviors and social dynamics.

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This work introduces a new measure of degree assortativity that accounts for correlations among nodes relevant to a spreading cascade, and shows that the critical point defining the onset of global cascades has a monotone relationship to this newAssortativity measure.

### The transsortative structure of networks

- Computer ScienceProceedings of the Royal Society A
- 2020

This work defines a property called transsortativity that describes correlations among a node’s neighbours that can significantly impact the spread of contagions as well as the perceptions of neighbours, known as the majority illusion.

### Homophily explains perception biases in social networks

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This paper shows how homophily and disproportionate group sizes influence the emergence of perception biases in social networks, and explores under which structural conditions individuals can reduce their perception bias by taking the perception of their direct neighbors into account.

### What Does Perception Bias on Social Networks Tell Us About Friend Count Satisfaction?

- PsychologyWWW
- 2022

Social network platforms have enabled large-scale measurement of user-to-user networks such as friendships. Less studied is user sentiment about their networks, such as a user’s satisfaction with…

### “What Do Your Friends Think?”: Efficient Polling Methods for Networks Using Friendship Paradox

- Computer ScienceIEEE Transactions on Knowledge and Data Engineering
- 2021

A novel neighborhood expectation polling (NEP) strategy that asks randomly sampled individuals: what is your estimate of the fraction of votes for A, and two NEP algorithms based on a graph theoretic consequence called friendship paradox are proposed.

### Analytical approach to the generalized friendship paradox in networks with correlated attributes

- ArtPhysical Review E
- 2021

This research presents a novel and scalable approach to solve the challenge of integrating big data and artificial intelligence (AI) systems into everyday life.

### The degree-wise effect of a second step for a random walk on a graph

- MathematicsJournal of Applied Probability
- 2018

It is proved that under the configuration model, for any fixed degree sequence the probability of exceeding a given degree threshold is smaller after two steps than after one.

### THE FRIENDSHIP PARADOX FOR WEIGHTED AND DIRECTED NETWORKS

- PhilosophyProbability in the Engineering and Informational Sciences
- 2018

This paper studies the friendship paradox for weighted and directed networks, from a probabilistic perspective. We consolidate and extend recent results of Cao and Ross and Kramer, Cutler and…

### The Buss Reduction for the k-Weighted Vertex Cover Problem

- MathematicsISAIM
- 2018

The Buss reduction is generalized to the kWVC problem and its properties are studied on surrogates of large real-world graphs that are generated using the Erdős-Rényi model and the Barabási-Albert model.

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