Neighbor-Neighbor Correlations Explain Measurement Bias in Networks

  title={Neighbor-Neighbor Correlations Explain Measurement Bias in Networks},
  author={Xin-Zeng Wu and Allon G. Percus and Kristina Lerman},
  journal={Scientific Reports},
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…

Impact of perception models on friendship paradox and opinion formation.

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.

Degree Correlations Amplify the Growth of Cascades in Networks

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

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

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?

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

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

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

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.


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

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.



Generalized friendship paradox in networks with tunable degree-attribute correlation

  • Hang-Hyun JoY. Eom
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2014
At the individual level, the relevance of degree- attribute correlation to the paradox holding probability may depend on whether the network is assortative or dissortative, and this work numerically studies the correlated model of networks with tunable degree-degree and degree-attribute correlations.

Network Weirdness: Exploring the Origins of Network Paradoxes

This paper proposes a strong form of network paradoxes, based on utilizing the median, and validate it empirically using data from two online social networks, and demonstrates that strong paradoxes arise due to the assortativity of user attributes, including degree, and correlation between degree and attribute.

Generalized friendship paradox in complex networks: The case of scientific collaboration

By analyzing two coauthorship networks of Physical Review journals and Google Scholar profiles, it is found that the generalized friendship paradox (GFP) holds at the individual and network levels for various characteristics, including the number of coauthors, theNumber of citations, and the number-of- publications.

The "Majority Illusion" in Social Networks

A statistical model is developed that quantifies the effect of the majority illusion and shows that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure.

A simple model of global cascades on random networks

  • D. Watts
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 2002
It is shown that heterogeneity plays an ambiguous role in determining a system's stability: increasingly heterogeneous thresholds make the system more vulnerable to global cascades; but anincreasingly heterogeneous degree distribution makes it less vulnerable.

Virality Prediction and Community Structure in Social Networks

It is demonstrated that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration, and the more communities a meme permeates, the more viral it is.

Core-Periphery Structure in Networks

This paper develops a new method to investigate the meso-scale feature known as core-periphery structure, which entails identifying densely connected core nodes and sparsely connected peripheral nodes in a network.

Identification of core-periphery structure in networks

The method is found to be efficient, scaling easily to networks with a million or more nodes, and it is demonstrated that the method is immune to the detectability transition observed in the related community detection problem, which prevents the detection of community structure when that structure is too weak.

Network Structure, Topology and Dynamics in Generalized Models of Synchronization

This work proposes a model of synchronization in a network of oscillators coupled via nonconservative processes and shows that the traditional and nonconservative models of synchronization reveal different structures within the same network.

Friendship Paradox Redux: Your Friends Are More Interesting Than You

Feld's friendship paradox states that "your friends have more friends than you, on average." This paradox arises because extremely popular people, despite being rare, are overrepresented when