As we move toward a society where no person or firm acts in isolation, it is vital to understand the systems in which people and firms interact. These systems can be represented as networks where the entities are called nodes and interactions among them are represented in terms of ties. More generally, a node can be a neuron, an individual, a group, an organisation, or even a country, whereas ties can take the form of friendship, communication, collaboration, alliance, or trade, to name only a few. Most network studies focus solely on a single type of nodes, and whether or not two nodes are connected. While these studies have uncovered numerous organising principles, the measurement crudeness might lead to inaccurate conclusions. Often researchers possess richer types of data, but are unable to analyse it due to the lack of methods or tools. The aim of this site and software is to highlight areas where tie strength, multiple types of nodes, and the evolution of networks can be considered.
I have structured the site in five main sections with the first three devoted to weighted, two-mode, and longitudinal network methods, respectively, and the remaining to the R-package tnet and network datasets. Admittedly, this site is mostly based on my own work, but do drop me an email or leave a comment if you have a question/remark or you feel like I’m missing a point somewhere.
Weighted Networks
Which is the shortest path from the lower-left node to the central node? Directly or via the top-left node?
A major limitation of many methods used for studying networks stems from the fact that the strength of ties is not taken into account. Granovetter (1973) argued that the strength of a social tie is a function of its duration, emotional intensity, intimacy, and exchange of services. For non-social networks, the strength often reflects the function performed by the ties, e.g. carbon flow (mg/m²/day) between species in food webs (Luczkowich et al., 2003) or the number of synapses and gap junctions in a neural networks (Watts and Strogatz, 1998). In infrastructure and information networks, variations in the strength of a tie depend on the flow of information, energy, people, and goods along that tie (Barrat et al., 2004). The strength of a tie is generally operationalised into a weight that is attached to the tie, thereby creating a weighted network. There are clear advantages to incorporating tie weights in network analysis. For example, the transmission probability a disease between two people is related to their interaction-level. This section highlights both generalisations of binary measures and novel measures for weighted networks as well as various random network types.
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Two-mode Networks
Network with two types of nodes
Another limitation of network analysis has been the focus of a single type of nodes (e.g., people) and the direct ties between them (i.e., one-mode networks). Nodes are rarely directly connected, and instead connected through various media or projects. This type of networks is often referred to as two-mode, affiliation, or bipartite networks. One type of two-mode networks that has received great attention is scientific collaboration networks consisting of authors and publications where the authors are connected to papers, and not directly to each other (Newman, 2001). However, given the lack of methods for two-mode networks, the networks are often analysed as a one-mode network by, for example, simply connecting authors with their co-authors. While this procedure allows the networks to be analysed, the underpinning assumptions in two-mode networks differ from that of prototypical one-mode networks. In one-mode networks, a person is connected to one other person when a tie is formed. Conversely, in a two-mode network, ties are formed to all the other authors if a new author joins an existing publication. Moreover, these networks tend to have many fully connected cliques. In fact, the level of clustering tends to be much higher in two-mode networks than in other networks. The section of the site outlines various methods for analysing two-mode networks.
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Longitudinal Networks
May 11, 2012: This is the last part of the website to be completed. The code is mostly there, but I am still working on the text etc.
Networks evolve.
Network analysis is mainly static. Most studies record the nodes and ties at a specific time (e.g., on the day of interviews). However, networks are a result of many actions (e.g., nodes joining and leaving as well as forming, reinforcing, weakening, and severing ties). Analysing aggreations of these actions represents a limitation as they are not independent from each other, and the dependency structure among them is unknown. This limitation is especially relevant for studies trying to investigate why certain ties are formed (see the literature on ERGMs and SIENA). While collecting static data is often the only feasible method, the exact evolution of the network is sometimes available. For example, the time of communication is recorded on online social network sites, such as Facebook, or in phone call logs. In fact, by having the time of each tie, it is possible to understand the dependency structure in the network. Nevertheless, there is also a lack of methods for networks where the time of each tie is known. As such, these networks are often aggregated to a static network, or multiple static networks (e.g., daily snapshots; Kossinets and Watts, 2006; Panzarasa et al., 2009). This section present some of the methods and tools to study these networks as well as random network models.
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Software
The usefulness of methodological advances is lessened if they are not implemented in an easily accessible programme. To walk the walk, each section contains the specific details for applying the measures in the free open-source statistical programme R. I have chosen R to ensure that everyone can easily access it (even those on Mac and Linux). The example code relies mainly on the R-package tnet that can be downloaded directly from within R using the Comprehensive R Archive Network (CRAN)-servers. The software section outlines how to install and prepare your data as well as how to import from and export to other network analysis programmes. Read more…
Datasets
To further help researchers in the field, I have collected a number of weighted, two-mode, and longitudinal networks. These are generally publically available networks or networks that I have collected. The networks are accompanied by an in-depth description of how they were collected and defined. Read more…
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1.
John McCreery | November 3, 2011 at 8:43 am
Tore, I have just discovered your site. A truly amazing resource. I am wondering if I might consult with you occasionally re my current project. A Google search for “John McCreery SlideShare” will bring you to biographical information about myself and a series of presentations describing the project. Briefly, as things now stand, I have data on six moderately large networks based on the credits data in the 1981, 1986, 1991, 1996, 2001, and 2006 Tokyo Copywriters Club Advertising Copy Annual: 30,000 role relationships linking 8000 creators to 4000 prize-winning ads. I also have a small library of published material about the Japanese advertising industry, its star creators, and what they say about what they do and, having spent nearly three decades working in and around the industry, personal contacts that enable me to secure interviews with individuals who are central figures in the networks I am looking at. That said, I am, when it comes to network analysis, a self-taught independent scholar with what remains only a sketchy knowledge of the field—which is, for example, why I have just discovered you. In any case, I am truly delighted to discover this site. I anticipate learning much from you.
2.
Tore Opsahl | November 3, 2011 at 5:04 pm
Hi John,
Thank you for taking an interest in my work. The dataset sounds really interesting, and a good source of understanding team formation and success. Also, the mixed methods approach is great for guiding the research. Please send me an email so we can connect privately.
Best,
Tore
3.
Vaclav | November 24, 2011 at 2:19 pm
Hi, thank you for developing nice tools for NA. It seems to me, however, that your network format ignores zero-degree nodes, right?
4.
Tore Opsahl | November 24, 2011 at 10:39 pm
Hi Vaclav,
Thanks! The edgelist format does indeed not include isolates when they are at the end of the node id sequence. If you have a look at the example in the Closeness centrality in networks with disconnected components-blog post, you can see how you can include isolates by making sure a non-isolate has the highest node id.
Best,
Tore
5.
Vaclav | December 8, 2011 at 9:04 pm
Hi again,
you are absolutely right. It’s only problem of those last nodes. Thanks again, your package is very helpful to my research!
All best,
Vaclav
6.
Alexander Smit | May 17, 2012 at 9:26 pm
Hi Tore,
thank you very much for all the good work you are doing with tnet! Since the data I use for my PhD is either 2-mode or (when projected to one of the modes) weighted, you can imagine I make grateful use of the package.
What I was wondering: do you think there will be a day where we can calculate network-level measures for weighted networks, such as density or degree centralization? In the work on interorganizational networks my experience is that usually the values get dichotomized right away so they can be tackled with the traditional network-level measures. But somehow that does not feel completely right.
I am curious what your vision is on this subject!
Best,
Alexander Smit
7.
Tore Opsahl | May 18, 2012 at 2:18 pm
Hi Alexander,
Great question and one that is partly unanswered. When I say partly, it is because degree centralization metrics have yet to be generalized to two-mode networks and weighted one-mode networks and density to weighted networks; however, there is a host of network-level or global metrics for two-mode and weighted one-mode networks. For example, average degree is a better metric than network centralization (in my mind) as degree has been shown not to scale exponentially with the number of nodes (i.e., n*(n-1); see Node Centrality in Two-mode Networks). Additionally, there are global clustering metrics for both two-mode networks and weighted one-mode networks that might be applicable to you.
Hope this gives you some new ideas!
Tore