- Massimo Pizzol added an answer:What are the applications of network analysis?
There is basically no limit to the phenomena that can be modeled and analyzed in terms of complex networks - entities and their relationships between which can be represented as the nodes and edges of a graph, and which form a non-trivial pattern. So let's make this a small survey:
- Where in your research do you employ complex networks and network analysis methods?
- What are your data sources? How big are they?
- Which tools do you use for the network analysis process?
- What did you learn from the network analysis?
- Wu-Chen Su added an answer:What are the current active research areas in social network analysis?I'm trying to choose my MSc research area in the field of social network analysis but I'm new to this subject and am not aware of currently active research areas or the trends. I would appreciate any suggestion you might give, so I can choose the one closest to my interests.
You may consider my paper for current issues and trends of multiple online social network study to find suitable topics for your study.Following
- Jose Javier Ramasco added an answer:Is anyone working on Ebola contact-tracing with mobile phones?
Is anyone working on Contact Tracing, in the current Ebola outbreaks, using non-interview (non-F2F) techniques (such as tracking mobile phones) to gather information on movements/interactions of infectious individuals?
See this paper at PLoS Currents Outbreak:
- Gandhi Kishan Bipinchandra added an answer:Are there any good visualizations showing the turbulence of real-life scale free networks?
I am looking for good (dynamic - i.e. dynamic gif or video) visualizations that show how turbulent real-life international scale-free networks can be. For instance, the global propagation of computer viruses; of 'viral' memes, etc. Can anybody help me?
have a look it
- Maurizio Campolo added an answer:Which software are you using for complex network analysis?There is a variety of software packages which provide graph algorithms and network analysis capabilities. As a developer of network analysis algorithms and software, I wonder which tools are most popular with researchers working on real-world data. What are your requirements with respect to usability, scalability etc.? What are the desired features? Are there analysis tasks which you would like to do but are beyond the capabilities of your current tools?
I apply Brain Connectivity Toolbox (BCT, http://www.brain-connectivity-toolbox.net/ source C++, Matlab and Octave) contains a large selection of complex network measures, statistic and comparation, by Sporn/Rubinov.
I use another source: http://strategic.mit.edu/downloads.php?page=matlab_networks .Following
- Hanno Krieger added an answer:Social media: Downvoting and degrading in social networks – What are the reasons? - What are the reactions?We have recently seen a lot of down-votings in some threads. We also saw different kinds of reactions on this phenomenon. I would like to ask a question about these reactions.
First I assume that there are several possible reasons for downvotings: dissent – misunderstandings – misuse of buttons without knowing it– a social scientist who writes a paper about the reactions – a test carried out by RG – some technical problem, etc.
I would like to have a discussion about the reactions on the part of researchers. We have seen calls to ban down-voters or to cancel their anonymity. Elections in democracies are anonymous and there are good reasons for this. I think most of us agree about some basic traits of democracy, the right to stay anonymous is among them. What about these basics in social media?
I agree to your statement about the tasks of RG, but I think that "difference of opinion" is not healthy, as you can ascertain, when reading the actual two partner dialogue.
But in an open team differences in opinion can be helpful and necessary. So please dear disputers, stop the personal consternations and your beeing offended.Following
- Abdulmunem Khudhair added an answer:Book RecommendationsCan anybody recomend me a book about networks in general?
Thanks a lot
Go for Cisco v5Following
- Ritesh Kumar added an answer:Is there any method for finding optimal number of communities using network based community detection methods such as louvain method?
Graph based community detection methods are very effective in explaining the underlying structure of graph but i have not come across any method find optimal number of community similar to clustering methods.
I am sorry, I should have made it a bit clear.
Say, I am trying to identify the communities in an unsupervised manner and for that I am trying to maximize the modularity. Now, I get different number of communities with different nodes even at a single resolution parameter. The question arises which of the communities is the best i.e. is there any statistical criteria which can lead me to find that number?
Moreover, the choice of resolution parameter itself is a question mark.Following
- Valdis Krebs added an answer:What are some of the best models describing the epidemic spread over a network?Epidemic in Networks
Looking for some of the best papers or thesis to go through.
The spread of TB/HIV in human networks...
Follow links to original papers with the CDC at bottom of article.Following
- Rene Von Schomberg asked a question:"Science in Transition": does open science make science better?
The emergence of "facebook for scientist" such a research gate, academia.edu etc. will enable scientist to engage in global,networked and collaborative mission-oriented scientific projects and hopefully, thereby leaving the frustration of the need to publish in high-impact journals behind. But can it make science better?
The European Commission has launched an online consulation (see link below)
Open, networked science could make science better because:
- more reliable (as it allows early, and better and more effective data-verification)
– more efficient, as it can prevent planned, useless duplication of similar research efforts elsewhere on the globe and extend collaboration to a broader range of collaborators
- moree responsive to the societal demands of citizens, as science could become more transparent and open as before
- more credible, as issues of scientific integrity could be better tackled in an open and transparent context.
- more inclusive in the incorporation of a broader range of scientific knowledge producers beyond the academic context and including, for example, citizen scientists and scientists with limited financial support.
-facilitate globally organised mission oriented research, having scientists sharing knowledge and data prior to publication and thus advancing science at a faster pace. ( Human genome project which included a moratorium on publishing, was an early example).
What are your views?
The European Commission has launched a public consultation to better understand the developments to which the Research Commissioner refers to as ” ‘Science 2.0′: the next scientific transformation”. I encourage anyone to respond to this consultation.
The Commission will validate the outcome of the consultation during a series of Workshops across EuropeFollowing
- VARDHARAJULU K N added an answer:In cluster sampling method, On what basis we calculate the number of clusters to be selected?
Please help me
In any clustering techniques first we randomly elect some of cluster heads or any member who initiates clustering process.Next step is to check all the remaining members characteristics with initiative members. They join with CH which has more similarities. Here we cant calculate number of clusters to be selected (up to my knowledge).Following
- Ingo Vogt added an answer:Tools for analyzing large scale networksWhat kind of tool would you suggest for the analysis of large scale biological networks? The tools I have used so far are Cytoscape and Network Workbench. Both have some really nice features but also disadvantages. I would like to discuss with others about this topic and if there are recommendations for specific types of networks making a tool advantageous compared to others?Following
- András Bozsik added an answer:Why do RG participants prefer general or common questions?
The answer seems to be simple:
The most characteristic speciality of RG is its scoring system. This scoring system is determined by an algorithm based on some not sufficiently modelled and assessed objectives which oscillate in their impacts. This oscillation means for many of us as an uncountable deformation and the source of uncertainty.
Participants are “measured” on the basis of up votes granted by peers. The objectivity of this up votes cannot be the subject of this evaluation.
Where can get a lot of up votes? Where there is a big traffic! Big traffic = a very high audience. Thus, the question should be very general, without needing deep and specialised knowledge and practically no or very little looking for references and other preparations.
Specialised and strictly scientific threads do not attract many participants and because of economising time and risk many do not comment them. Even there are strictly scientific threads where the answer itself is important and participants do not score at all.
Thus, because of score number is determining and central this is which motivate participants.
In addition: participants do not know how the up votes are transformed into RG scores. This is certainly a willingly built up uncertainty into the system which stimulates intensive traffic and participation but not the merit and quality of threads and answers!
I think the inconvenience of scoring system has a imperfect feedback which pushed RG activity on a not wanted orbit and an originally scientific forum converges towards a Face book like system.
Addition2: Score collection became an addiction for many of us and valuable time is wasted for often superficial and over-sophisticated chats.
Your opinions are appreciated!
Thanks for your lines. This is not a pure politeness but because I like your style as it is plus because of your human and humorous side. I like what is useful, interesting and may be a challenge. Painting is a challenge, in addition it is a creative activity too. I am still at the beginning of it and I have to work still a lot. Now, I paint so called studies: flowers, birds, landscapes and simple portraits. I prepare 1, 2-3 small pictures each day. And regarding flowers, they are not too difficult – at my level - to paint. Today 5 minutes ago I finished the portrait of my wife. I feel I am lucky because I look for aquarelle paper because of my increased “production”. The lovely little girl taking your hand – must be your grand daughter? My congratulations! It was a pleasure to change words with you!Following
- Valdis Krebs added an answer:Is general sparsification of complex networks possible?
Nowadays complex network data sets are reaching enormous sizes, so that their analysis challenges algorithms, software and hardware. One possible approach to this is to reduce the amount of data while preserving important information. I'm specifically interested in methods that, given a complex network as input, filter out a significant fraction of the edges while preserving structural properties such as degree distribution, components, clustering coefficients, community structure and centrality. Another term used in this context is "backbone", a subset of important edges that represent the network structure.
There are methods to sparsify/filter/sample edges and preserve a specific property. But are there any methods that aim to preserve a large set of diverse properties?
Let's assume you have a large human network such as cell-phones or Facebook users and your network is > 1M nodes. This network gives the FALSE impression that anyone is connected to anyone else, by some path, in the network. But most FB and cell-phone activity happens in local clusters (those 1 and 2 steps away from you). So rather than look at 1 large component of 1M nodes it makes more sense to look at the natural clusters of dozens/hundreds of people who really know each other (or at least of each other). So how do you reduce the large component to hundreds/thousands of real life social circles? A human network of > 1000 nodes usually makes no sense from a sociological perspective -- because most people are strangers in large networks.
1) Often the reason we end up with one very large component is that we have set the bar TOO LOW for what an edge is. If you have edge strength/frequency to work with, move it up to get rid of the noisey connections while retaining at least "weak ties".
2) Many networks have many satellites/hangers on with a degree of 1... hide all of those (you get one or more 2-cores). This will start to show the natural emergent clusters in the data. If you still have one large component you may try a 3-core or 4-core... but no more.
3) Betweenness is normally calculated using all geodesics in a component. Yet, it can also be calculated using just the shorter geodesics (maxing out at 3 steps). This efficiently find the spanners between clusters. There iterative removal will leave you with hundreds of natural social circles. Keep the high betweenness nodes you removed as a separate network -- as the backbone.
4) Investigate the smaller clusters and the backbone to understand both local behavior and global structure.Following
- Abhishek Dwaraki added an answer:Is an Erdos-Renyi Communication network possible?We know very well that a communication network is always assumed to be or also exhibits the nature of Scale Free Networks. But is it possible that a communication network can be framed as ER Network or a Random Network?
Links to any papers or thesis that model communication networks as ER or Random model would help too.I think this might shed some useful information on whether networks can be modeled on the Erdos-Renyi model and can be scale free or not. It is basically a review paper, but has some interesting points that can be made use of.Following
- Henning Meyerhenke added an answer:Datasets of networks for benchmarking community detection algorithmsIs someone knows where to find datasets of networks with known communities (that's the important point), in order to have reference clusters to validate/invalidate community detection algorithms ?Following
- Alex Shackelford added an answer:Can anybody please name some common applications of Complex Networks (aka. network science) preferably related (but not limited) to computer science?I'm trying to write a report and I need some hot topics/applications to emphasize. Does anybody also know about the particularly active area of research in Complex Networks?Im not really sure what you mean by complex networks, but Ive been working for Internet service providers for the past year, and I know if you figured out how to network neighborhoods of people together, where they would efficiently share bandwidth, I think that would be very useful.Following
- Manikant Prasad added an answer:SIR or SIS model, which one is more accurate for explaining the spread of computer virus in a network?In SIS/SIRS model the network components which are infected are assumed to recover and go to susceptible state based immediately or after some time based on immunity of the virus.
While in SIR model, once recovered, the nodes are assumed to have become immune to the same disease and no longer participates in the spread of epidemic.
I was just wondering which model describes the computer virus epidemic more accurately.Thanks sir for your answer :)Following
- Peteris Daugulis added an answer:What are current algorithmic challenges in connectome analysis?The human connectome is a comprehensive map of the neural connections in the brain - in other words, a graph. Coming from a background in graph algorithm development and network analysis, the field of connectome analysis seems to me a very interesting application domain. However, it is a domain I am just beginning to understand. Therefore I hope to get some feedback from both neuro- and computer scientists, starting with the following questions:
- It is my understanding that at the neural scale, the connectome is a graph of more than 10^10 nodes and 10^14 edges. If it could be comprehensively mapped at this scale - which i believe it cannot at this point due to a lack of imaging technology - would it be in the range of current computing capabilities to analyse such a network?
- Has the connectome been mapped at coarser scales? If yes, what graph sizes are we talking about?
- Are standard measures from network analysis (such as degree distribution, diameter, clustering coefficients, centrality, communities) relevant for connectome analysis? What are interpretations of such measures?
- What are other structures of interest in the connectome that could be revealed by graph algorithms? Is there a need for domain-specific algorithms to discover brain-specific graph structures?
- Are there publicly available datasets that represent the connectome as a graph?About the need for previous expert knowledge - not necessarily so. For example, you don't need expert knowledge to find a new motif, just search for motifs.Following
- Kamal Eddin Bani-Hani added an answer:How risky is our digital world?With all the advances associated with the digital world, is it still risky? Everything can end suddenly and efforts over years may be lost and wasted. What do you think about that and how to avoid such a possibility?The future is full of new technology, advancement and we are just at the begening of the e-revolutionFollowing
- Ergys Rexhepi added an answer:Is there any simulator for home networks?I know NS and GloMoSim for Ad-hoc networks. Is there a simulator used for home networks?Try GNS3 if you have a powerful computer (except for catalyst switches) or you can perform everything you want with Packet Tracer, free cisco product.Following
- Deepankar Mitra added an answer:Why not take advantage of electrical signals to charge mobile devices from the open air?As long as the air carries electrical signals, why not take advantage of these electrical signals to charge mobile devices, "charging in case of emergency," for example.I guess you must have heard about "witricity" or wireless electricity. But the problem with it is, it can't be deployed everywhere. It needs a proper set up. Check out this Wiki link-
and this one also:
- Mehdi Hedayatpoor added an answer:What is the importance of ecological memory to anthropogenic disturbance?Ecological memory (EM) is an important and relatively new concept in ecology. How can we apply our understanding of EM to anthropogenic disturbances? Do anthropogenic disturbances alter EM in some systems? Is EM erased by some types of anthropogenic disturbance? Can we design anthropogenic disturbances to optimize EM?Precipitation, soil water, and other factors affect plant and ecosystem processes at multiple time scales. A common assumption is that water availability at a given time directly affects processes at that time. Recent work, especially in pulse-driven, semiarid systems, shows that antecedent water availability, averaged over several days to a couple weeks, can be just as or more important than current water status. Precipitation patterns of previous seasons or past years can also impact plant and ecosystem functioning in many systems. However, we lack an analytical framework for quantifying the importance of and time-scale over which past conditions affect current processes. This study explores the ecological memory of a variety of plant and ecosystem processes. We use memory as a metaphor to describe the time-scale over which antecedent conditions affect the current process. Existing approaches for incorporating antecedent effects arbitrarily select the antecedent integration period (e.g., the past 2 weeks) and the relative importance of past conditions (e.g., assign equal or linearly decreasing weights to past events). In contrast, we utilize a hierarchical Bayesian approach to integrate field data with process-based models, yielding posterior distributions for model parameters, including the duration of the ecological memory (integration period) and the relative importance of past events (weights) to this memory. We apply our approach to data spanning diverse temporal scales and four semiarid sites in the western US: leaf-level stomatal conductance (gs, sub-hourly scale), soil respiration (Rs, hourly to daily scale), and net primary productivity (NPP) and tree-ring widths (annual scale). For gs, antecedent factors (daily rainfall and temperature, hourly vapor pressure deficit) and current soil water explained up to 72% of the variation in gs in the Chihuahuan Desert, with a memory of 10 hours for a grass and 4 days for a shrub. Antecedent factors (past soil water, temperature, photosynthesis rates) explained 73-80% of the variation in sub-daily and daily Rs. Rs beneath shrubs had a moisture and temperature memory of a few weeks, while Rs in open space and beneath grasses had a memory of 6 weeks. For pinyon pine ring widths, the current and previous year accounted for 85% of the precipitation memory; for the current year, precipitation received between February and June was most important. A similar result emerged for NPP in the short grass steppe. In both sites, tree growth and NPP had a memory of 3 years such that precipitation received >3 years ago had little influence. Understanding ecosystem dynamics requires knowledge of the temporal scales over which environmental factors influence ecological processes, and our approach to quantifying ecological memory provides a means to identify underlying mechanisms.Following
- Moon 14 added an answer:Evaluation matrix for new MAC protocol in wireless sensor networksWhat performance metrics are to be measured for the new MAC protocol for wireless sensor networks with multi-rate sensor nodes?
Especially I focus on proportional fairness between nodes.Thanks a lot
What about throughput?Following
- Christopher Landauer added an answer:How fast does knowledge grow in a network?In a directed network of agents passing knowledge to each other how fast does information in a node grow with the number of edges incident on it? Linearly or exponentially with the number of edges? If on one hand the knowledge of the node agent seems to grow with the sum of the knowledge shared through the incoming edges, on the other hand each item of information shared through one edge might recombine with each item of other edges incoming information, to form new items of information. For example if agent A tells me that there is a traffic jam near the shopping center and agent B tells me that today morning there are big sales there, I get to acquire a third peace of information from combining what agent A and Agent B told me, which is that the traffic jam is caused by the sales and won't stop until the sails are over. Any ideas on which model better suits reality? Sorry if this sounds as a rather naïve question, but since it is related, but not central to my research, I did not get to do a literature search on it.a while ago, i wrote some papers on computational semiotics that are at peripherally relevant. The ``Get Stuck'' Theorems show (1) that no matter what finite representational mechanism you use, if you are trying to represent more and more complex phenomena, you will get stuck (the size of the description exceeds your computational capacity to compute with it), and second, that even if humans are adding to a base of knowledge, eventually the knowledge will become too cumbersome to change, mainly because there will be too many interconnections
Cognitive Technology: Instruments of Mind: 4th International Conference, CT ...
edited by Meurig Beynon, Chrystopher L. Nehaniv, Kerstin Dautenhahn, Springer LNAI 2117 (2001)
the relevance is that a faster increase in knowledge can lead to getting stuck sooner, so it isn't always the most desirable behaviorFollowing
- Sedat Acar asked a question:Wrong arrow direction in Gephi?I am using buyer-supplier data. Gephi is reading and visualising a firm's data inversely. This mistake affects the results. How can I solve this problem?Following
- Esra Kahya-Özyirmidokuz added an answer:Anyone interested in collaborating on brain research of online social networks?I study ongoing neuronal activity in brain networks of healthy subjects and neuropsychiatric patients.
I intend to apply for a Young Investigator grant from the Human Frontier Science Program (HFSP), with an interdisciplinary approach combining network science of online social networks and brain networks.
Therefore I am looking for collaborators from the fields of computer science, sociology, anthropology or psychology in their early stage of an independent research career (requested by HFSP, i.e. post PostDoc, Assistant Prof,...), that might be interested in collaborating on this topic.Dear Valentin Riedl,
i think i am very late for the topic..
But i am interested in the subject.
- Steve George added an answer:Updating publications onlineCan anyone recommend a site for documenting my research online so that it will be visible to all viewers?If Researchgate is the best then the others must be bl**dy awful. As far as I can work out it's impossible to see a list without the abstracts without turning them off INDIVIDUALLY! I know my publication list is missing about fifty entries, but it's impossible to view it to see which ones. Totally user-unfriendly.Following
- Muhammad Riaz added an answer:What is the expected impact of agile organisations on enterprise architecture?enterprise architecture can itself be made agile by optimising the organisation it represents, to make it agile. Businessmen however, expect something from this process. ROI? organisational effectiveness? resilinece to change?Agile organisations are adaptive and responsive to changes in the business environment. Agile organisation will tend to improve the enterprise architecture based on the customer feedback and needs. As change is way of life for an agile organisation, Organisational efficiency and effectiveness, and resilience are incorporated in the enterprise architecture.Following
- Horst Fickenscher added an answer:Is there a simulator for evaluating a social network?I want to evaluate the performance of a social network. I want to know whether there exists a simulator for evaluating the performance of a social network. What are the performance parameters and how are they evaluated?Following
About Network Science
Physical, engineered, information, biological, cognitive, semantic and social network research.