color the nodes based on the group, this is what I want to realize in MOOC data, identifying the returning learners

This sounds powerful, I will rethink my choice, before make a decision I may need to get to know more about UCINET and NetMiner, make a comparison.

I may prefer to try this package later since my two friends used this software for years, I think I could get more help when I am stuck.

This is related to my project, exploring how the structural and positions of coauthor-ship network affect scholars' academic performance.

So dose the basic statistical package in R work, If I am not mistaken, I think we also can't use statistical package to make estimates for relational data.

I hope there is a further explanation about the negative and positive result since the sender efficacy is negative.

This makes a connection to my prior knowledge about traditional statistics, binary Logistic regression(dependent variable is binary, 0 or 1) and multiple Logistic regression(dependent variable has multiple levels, just like the school leader network example, four possible outcomes).

The difference between QAP and MR-QAP just like simple linear regression and multiple linear regression. When you have independent (network) variables more than one, MR-QAP is needed.

when reading this, I don't know why but a question suddenly came into my mind, why do we need so complicated/fancy models in social science research, specifically, except core independent variables and outcome variables, why do we use covariate/ control variables in a given model. I had an insight from a professor's explanation: for natural science, most objects of study are homogeneous and scientists can have a good control of interference in lab environment with careful experimental design. However, in terms of social science phenomenons, they are so complicated and are impacted by so many factors, including which we already know, and also a lot of which we don't know yet, let alone the subjects of social science study are so unique and heterogeneous. So we have to use advanced model to get closer to understanding those complex phenomenons, and we have to try our best to control the covariates we already know to carefully test the real relationship between independent variables and dependent variable. In addition, because we can not know or measure all factors that will impact a certain complex phenomenon, this is one of the reasons that a model is a simplification or approximation of reality and hence will not reflect all of reality.

a good conclusion, and I will keep this in mind while going through next chapter.

This sentences articulates the key character of statistics, I like it.

This sentence answers a question I proposed a few weeks ago.

I can't insert permutation and combination formula here, I will try to explain my confusion. I don't understand why use C 5 3 instead of C 5 2, although C 5 3 equals to C 5 2, I still think only using C 5 2 can make sense in this setting. Maybe I get something wrong, please correct me if so.

I think we calculated closeness centrality of vertices (and also the closeness centralization of complete network) two week s ago, it used the similar computation method as this egocentric network closeness centrality. It seems like that when calculate closeness and betweenness, you have to include indirect connections of a given ego, not just direct connections in the egocentric network.

Based on what I have learnt, betweenness captures brokerage, and closeness captures reachability (it is about how far away the rest of the network is from a certain actor). However, we also talked about eigenvector centrality a few weeks ago (the thought of eigenvector centrality is that your importance is determined by your neighborhoods’ importance) , I was wondering why the author didn't introduce eigenvector centrality of egocentric network in this chapter. I think one actor's eigenvector can indicate this actor's potential value or importance, it can be very useful in some specific settings.

This reminds me of strong ties theory and weak ties theory(also called structure hole) we had read before which are competitive theories , but both theories can explain some certain social phenomenon. Strong ties theory can explain how strong ties affect people's behavior or attitudes etc., and weak ties play a role of bridge to disseminate (non-redundant) information. So I think what matters is your research question/interest, your research question will drive you to apply appropriate theory and interpretation.

These two types of measures sound like the traditional statistical methods analyzing independent variables(to get some descriptive results of central tendency and tendency of dispersion). However, I think egocentric data is relational data which violates independence assumption. What are the differences between relational data analysis and independent data analysis. I am not sure whether I propose my question appropriately and correctly, and I remember I had read some detailed information relating to this question before, but I am still confused about it. (According to these examples listing in this paragraph, maybe the author talks about attribute data, not relational data?)

In my project, I will define the ego neighborhood in this straightforward way--ego and alter are linked if they were co-authors on a published paper. I would like to explore the ego-centric network of a few top productive authors, to analyse their collaboration patterns. Specifically, do these top productive authors tend to have cross-disciplinary partnerships, inter-institutional partnerships, or with-in institutional partnerships?

I 'm confused to tell the difference between "top-down" approach and "bottom-up" approach, it seems like they begin with different unit, "top-down" approach starts first with the whole network and then try to find its subgroups, and "bottom-up" approach starts first with more micro unit like dyad. Do I understand correctly?

I think this equation only can apply to calculate density of directed network, if we need to calculate a non-directed network density, it should be another equation, is that right?

The link is not working.

It is common to use students' family income or parents' education to predict students' academic performances. Educational researchers also usually talked about culture capital and social capital, but I think It could be more interesting employing SNA method to explore what resources certain student can access except from parents and how those resources impact students' achievements. In addition, I think we need more research investigating the extent to which social capital is independent from socioeconomic status.

This reminds me of a question I have been being curious about, how do administrators of a university to make some big decisions especially when they attend board or other management meeting. I think it is interesting to explore this question using social network perspective.

Now this helps me to understand the stroke patients case, stroke people who go to hospital early have weak ties with other people, maybe because the other actors in the stroke people's networks are more likely to have resources (knowledge or experience refer to stroke symptom) which stroke people's family members don't have.

what about altruism ? maybe not all investments expect some return.

stroke?

I claim this one--Tianhui

stroke?

I think background characteristics or students' attributes also affect how friendship networks form. Like attracts like, students with some attributes in common are more likely to form a friendship network, so I think attributes also play a critical role in this case. But I also agree it is a good way to tell a story from the social network perspective.

This reminds me a course" the introduction of sociology", in that course, the instructor talked much about social relations embedding economy. He used Facebook (maturing cybernetworks) as an example to illustrate how Facebook uses social network to make money, it employs user data especially the friend relations to advertise and sell products to certain users. And a lot of vivid examples related to economic activities were presenting, such as how Chinese people or Jewish people use culture networks to conduct transactions abroad.

spelling mistake?

there is an extra blog