Questions related to Network Modeling
Hello, did these two articles discover the same theory? My academic performance is too shallow to be sure. I think that although the terminology used in these two articles is different, their views and ideas are similar. The difference is that the "dynamic interactive process network" mentioned in this article adds the viewpoint of "psychological network model" to several sentences. Thank you!
Mechanisms leading to the spastic movement disorder (SMD) as a positive symptom of the upper motor neuron syndrome are not yet fully understood. All, the underlying disease, the (spinal) lesion level, the lesion location within the brain, affected descending (parapyramidal) motor tracts, disrupted sensory input etc. seem to be more or less relevant for the emergence of a SMD.
Is there any research on computational network models of spinal/supraspinal spasticity on the base of current neuroanatomical and neurophysiological data known to be of relevance for the develoement of a spastic movement disorder?
i am looking for a code in Matlab or R-language to run the wavelet-Neural Network model. please if any of you have done a project relating to this model please share with me. you can also share the paper representing the outcome of your work, so that it can be read and cited appropriately. if you have done this code in another software other than Matlab or R-language , you can also share with me. i can learn such software. Also if you have another resource where i can get codes or learn how to develop this code please you can put the information here. Thanks scholars
My email: email@example.com
I would like to know if it is possible to use SAOMs (Stochastic actor oriented models) to analyse weighted networks?
Thank you in advance,
I Interested in to work in gene network modeling thus I need to know what is future of gene network modeling in agriculture
I am focusing on pore network modeling of a two-layered nonwoven medium.
Considering you have a two-layered fibrous medium with a given gap between and you intend to extract the representative pore network. How need to deal with the intermediate pores at the gap?
Experimental observations shows a dip of oil saturation at the gap.
I have enclosed a snapshot of the geometry. I wonder except extracting the network of the whole geometry, is there any alternative techniques?
For instance, is it possible to stitch the representative networks to each other upon extraction? If the answer is Yes, then how should the throats at the interface be defined in terms of connectivity, length, diameter, etc?
As shown it is an unstructured network and it is not easy to deal with the pores at the interface.
Recently an external collaborator performed for me a GC/MS on my samples, using Glucose and Glutammine with all C labelled.
The related data consist in peak area of each metabolite/isotopomer. I tried to analyze these data by INCA (a toolbox of MATLAB), but I need to find a metabolite network model that describes the pathway that I want to investigate (e.g. TCA Cycl and Glycolysis)
Do anyone know were I can find network models?
I would like to recive any suggestions about better MFA software analysis as well as INCA.
Thanks in advance
I would like to know if it is currenly possible to use temporal ERGMs (Exponential Random Graph Models) for analyzing weighted networks?
For now, it seems that software packages available to analyse TERGMs (tergm or btergm) only use binary networks.
Thanks in advance for your answer,
Hi all, currently carrying out some qualitative data analysis using NVivo and I was wondering how I could create a network model/diagram to show the relationships between my coding (such as a visual representation of: 'X is part of Y, Y results in Z' and so on).
I am interested to simulate distribution networks with distributed generation utilizing custom controls. Simulink is quite versatile in control system design and is friendly for people preferring visual approach to programming. But I am not always satisfied with how it solves power flow for detailed network models.
Lets say that I am looking for more reliable power flow solvers, but with control system design versatility of Simulink (preferably with visual programming). Any suggestions?
I am a physician and visiting research scientist at yale and together with my coauthors have published several studies in the field of psychology through Network Modeling and related analyses using r packages like bootnet, qgraph, NetworkComparisonTest, etc. I thought it might be a good idea to discuss different subjects related to this field, in a group. I am also interested in collaboration with other teams working in the field. Let's share our ideas, questions and suggestions in this group.
In the context of complexity, it is well known that each change that is given to nodes or relationships in a network will give emergent properties, and I am looking for a way to model these effects.
Since metabolic interactions are complex in multi-species systems, currently genome-scale metabolic models are applied to uncover the metabolism of each microbes. Then, can we build the cross-feeding models?
How can we increase the confidence for a new authenticated key exchange protocol in terms of the operational in the network model?
I'm looking for a recent review article that demonstrates what topics are in the focus of network modelling in the last years. I'm particularly interested in applications to socio-economic systems and ideas from econophysics.
Psychology has developed to the stage of "network model" and "process-based therapy". Psychology is about to enter Newton's time. If you are really interested in psychology and want to achieve something in the future, then you should focus on the frontiers of "network model" and "process-based therapy" (PBT).
The latter, in particular, will be the future of psychology, to which all schools of psychology converge. I have written a new theory of psychology, that is, from the development of PBT, please do have a look, this is very important。
Recently I came across of studying protein fluctations in Normal Mode analysis, Most of it was conducted in Elastic Network Models (Gaussian or Anisotropic). What insights we can develop by studying the protein fluctuations using ENM or quassi rigid decomposition. How is this different than Molecular Dynamics Simulation? Guide me through the entire concept.
I am performing pore shape analysis and creating pore network models of segmented microCT scans of carbonate plugs.For this purpose I require a parameter to estimate the complexity of a single pore.
I'm thinking the problem of mapping applications to geo-ditributed data center. However, most of work focuse on the network heterogeneity. And these network models are in old fashion. So I wonder any new network model is available to apply. And economic theory can be adopted on minimizing latency on geo-distributed datacenters.
I would ask if anyone can help. I have a huge EEG dataset, which I would used for training and testing Deep belief network model for predicting. I'm facing a challenge in the training this model using my PC and Matlab. I know that Matlab 2017 has Scale Parallel MATLAB Applications to Amazon EC2 Using Cloud Center, but not sure if it free for student and how to start doing this. Please help if you have any idea. Thanks
I am using Bayesian network for modeling risks in construction project. referring to the article or the thesis"
Estimating cost contingencies of residental building projects using Belief networks"
Are the method used for defining the probability distribution for the root nodes and conditional probability tables for intermediate nodes correct?
is it applicable to use the average value of probability for each node and feed the network manually? in other word, by taking the average, we learn the network by taking the average for each node and enter these number manually.
I need help to introduce me to some software that I can use for my research project. I just started doing a research about discrete fracture network to enhance in situ recovery of hard rock mining.
In my domain of experiment out of three input parameters one parameter is qualitative . Is it possible to construct a neuron network model taking qualitative term in to consideration including quantitative input parameters?
for example input parameters are color(dark, fair,brown),height and age.
I've seen some studies using pore network modelling and/or percolation theory with continuous time random work (CTRW) for modelling groundwater flow and mass transport, which is very interesting. I'd like to know comparing with classical methods, such as FE and FD with Advection and diffusion/dispersion equation (ADE), what is the advantage of this method? or for which kinds of problems, this method does better? And what is its limits or disadvantage?
Thanks a lot.
If I do pore scale modelling in a confined space for small plants or a reservoir, by direct pore space, reservoir modelling or network modelling using Navier-Stokes, Boltzmann method - is that valid for contaminant removal by using a porous membrane or can it be effective in water treatment?
Is it viable to succeed in near future in wastewater treatment areas as well?
The original, and current, Internet design has been mostly based on an honor system for the end points. The model being that the connection is less trusted than the end points, as access to the end points was granted under an honor system — and usage rules were effectively enforceable.
Reality showed that this model was upside down for commercial operation. The end points are less trusted than the connection. In fact, even if usage rules are enforceable at some connection points, the end points cannot be controlled. Anyone can connect to the network. There is no honor system. Usage rules are in fact not enforceable, users can hide and change their end points. The solution is to introduce trust as an explicit part of the design, which trust was implicit when the Internet was based on an honor system.
Of course, updating the Internet design to fit its current operating conditions is useful not only to stop spam. Social engineering and spoofing attacks also rely on the old honor system where users are trusted. "Trust no one" should be the initial state under the new Internet paradigm. The bottom line is that trust depends on corroboration with multiple channels (see Trust, above) while today we have neither (a) the multiple channels nor (b) the corroboration mechanisms. So, we lack trust because we can't communicate it.
Current work [1 and following, see RG home page] by Ed Gerck and team includes these topics, proposals and tests to combat spam, spoofing, and denial of service, as well as information-theoretic secure authentication integrated with authorization for access control.
Working with associating polymers to make physical gels, I often come across the label 'transient' for these networks. I was wondering if there is a way to determine wether a system is truly transient? I am aware of the transient network model in rheology which can help interpret data, but am wondering if there is a more direct way to determine the transient nature other than a correlation between this theory and experimental data?
I am currently analyzing collaboration networks in science. I have used SBM and ERGM to model a cumulative snapshot of the network. I know these methods alone are not sufficient to accurately model my network since it is a longitudinal network. I heard of Stochastic Actor-Based (or actor-oriented) Block Modeling (SABM) which can model my network taking into account it's dynamics in time. I have read some papers on it but have been unable to find a tutorial to guide me through its successful application on my data. Can anyone help?
I am trying to use Ieee802.15.4narrow as a base for my simulation. I want to get the RSSI value, but I don know how to print the RSSI when a packet is received in a node.
I am relatively new in Omnet++ and I am wondering if anyone could help me.
I am trying to estimate decompression time for fuselage with multiple chambers connected in serial and parallel network. Can we use pipe flow network model for this kind of compressible flow analysis?
I have 3 layer deep network model. Each model has hidden layers. could some one suggest is there any criteria to be followed for setting the hidden layer size.
In one paper i read we have to set the hidden layer size to 2/3 times the input size.
Is there any criteria required for setting hidden layer sizes. Or arbitarily can we take any hidden layer size
I tried MATPOWER but I don't think it's helpful for this.
I have a BN model in which there are four nodes. They are Job Arrival Rate, CPU Usage, Memory Usage and Resource Utilization. CPU usage and memory usage are dependent on Job Arrival Rate. Memory usage is also dependent on CPU Usage. Resource Utilization is dependent of CPU Usage and Memory usage. I have marginal probabilities of all the nodes. I want to calculate conditional probability table of the last node i. e., Resource Utilization which is dependent on both CPU Usage and Memory Usage. Please help me out.
Large-scale tunnel aerodynamics is generally one-dimensional, whereas near-scale airflows can be three-dimensional, especially due to buoyancy forces due to fire, or in complex geometries such as station boxes, or near obstacles such as vehicles, message signs, cable trays, etc. With the ever-increasing computational power available, it has become possible to solve the near-scale features by a three-dimensional CFD code, and keep the solver for large scale tunnel airflows one-dimensional, and use a code to pass outputs of one code to the inputs of the other in an iterative way. Where the type or format of data on each code is different, the effort of passing data between the 1D and 3D codes outweighs the benefits. What has been your experiences of this process, and what obstacles have you encountered? How do you see the future of multi-dimensional airflow network models?
I'm preparing a framework based on context- awareness , i need to model data (location from GPS, time, daily events from calendar, and human activities) using Bayesian Network or decision tree for inference high level context.
Could you help me to know which of them is suitable to me and candidate the suitable software to me?
Actually, Hello, I am working on Vehicle-to-Infrastructure (V2I) communication over heterogeneous wireless network. I would like to develop a handoff algorithm and simulate it using matlab.
I really need help!
Once an information flow (IF) can be represented as a process, can we measure your complexity as a network?
According Brandes, Robins, McCranie & Wasserman (2013), in order to think in terms of network first is necessary to have elements (E) and processes (P) of a network model: phenomenon (E), which passes through an abstraction (P), making the concept of the (E) which may be represented (P) and data network (E).
Taking the IF as a broad phenomenon, can we introduce the concept of network processes, which can be viewed as a web of interdependent tasks or activities, its products, which are more than the sum of its components and the participating organizations, can be understood as a network of people and communications?
Which measure can be more appropriate in this case?
I have to optimise paramters "(theta)" of a model of a railway network "F". This railway network model is given to me in the form of a MATLAB-p file, so it is a black box. I have been given two data sets from the model, input u and output y. The output of my railway network mode, "F" is say y_cap.
Then y_cap = F(u,theta).
I plan to take my optimisation function so as to minimise square of error between y and y_cap that is (y-y_cap)^2 and tune the parameters theta.
That is, objective function = (y-y_cap)^2.
Is my objective function convex or non-convex? How to comment on its convexity?
even myself, I always use MCMC to deal with all ERGMs I faced. However, today my Boss asked me a very simple question: "Why must you use ERGM and always solve with MCMC?", suddenly I found that I had no answer. After that, I felt that this question is quite interesting and I am curious about it as well.
Does anyone have some convincing answers?
Thanks in advance.
I had to draw a real-world network from a basemap in ArcGIS 10.2. After having added my shp files to a geometric network, I fixed the network build errors. Then, with the purpose of determining the degree of each node (e.g., the number of edges connected to that node), I ran the Spatial Statistics Tools->Utilities->Collect Events function. This gave me a table with the ICOUNT values, which correspond to the degree I was looking for.
My problem: the number of vertices worked out by the Collect Events function is far lower than the number of vertices my network actually has.
1) How can this be possible?
2) How could such function miss any point?
3) What do you think I could do to spare me the use of some network modeling tool?
PS: Using the Join function to see what vertices are not mapped in the ICOUNT table is out of question as the ObjectID key of the two tables does not refer to the same points.
Thank you for your time and dedication.
Edwin S. Shneidman (On the Nature of Suicide, 1969) proposed that 6-8 people are significantly affected by every suicide. This has generally become accepted as a benchmark in suicide prevention/postvention, but does not seem to have been the subject of much research. This "ground zero" for suicide loss comprises all or some of the victim's closest family and friends, which made up her/his social network in life. Communications theory studies social networks and one model (see link below for example) conceptualizes a "support clique" with about 5 members with very frequent contact with an individual and a "sympathy group" of about 12-15 people with at least monthly contact. Has any of the communications work been used in studies of suicide survivors?
Recently I have installed OPNET with visual Studio 10 in windows seven(64-bit).
I want to work on WLANs and for this i try to design network model given in the tutorials and also follow the same steps but at the end when i press the run button of OPNET after setting all the output variables gives bundle of error and did not show any of the graph i am attaching the tutorial as well as error sheet.
I am very thankful if you guide me that how can i simulate any network model in OPNET accurately....Please refer me some books materials so that i can get expertise in this tool and able to do my work.
Thanking you in anticipating.
I have weights and biases for my network model, training in MATLAB tool. I implemented the network model in MATLAB code and I compared the results with sim() function unfortunately I am getting wrong results.
Anyone implemented the network on microcontroller?
This question is related to the measure of estimation and forecast accuracy in (freight) transportation network models.
input data : origin-destination (O-D) matrixes at the European regional NUTS2 level for different transportation modes (road, rail, inland waterways). These matrixes contain tons transported for a given year between each O-D pairs. These matrixes exist for different types of commodity, but this is of less importance in my question. Consider this input as the "observed real-world" data.
Model : the matrixes are merged, so that I have one single matrix per category of commodities, that includes all what is transported by the three modes. Several mode-choice and assignment procedures are applied in a network model. Using a sample of 5% of the OD pairs, the model is calibrated for each mode-choice/assignment model that is tested, in order to reflect the "observed" modal-split found in the input data for the sample. The calibrated cost functions are then applied to the total matrix.
Output : For each mode-choice/assignment model, I retrieve the tons that are allocated to each OD pair, for each mode. I can also retrieve the tons that are assigned to each link (road, rail, waterway) of the network, but I've no observed counts along these segments, so that I cannot use this output.
It comes out that, even if classical estimators such as R2 (computed on a per O-D basis : observed tons / tons obtained by the models) can be very similar from model to model, the distribution of errors can be very different.
Question : I would like to compare the (relative) quality of the different models I test. A came over a series of estimators used in forecast accuracy (MSE, MAE, MAPE, MsAPE, sMAPE,...) and more recently MAPE-R and MASE, but I'm not sure that these estimators can be applied to the problem I describe here. Does someone have any experience with this ?
I have a question regarding the permutation traffic pattern.
Since the seminal work of Gupta and Kumar , extensive efforts have been devoted to study the throughput capacity of wireless networks restricted to the so-called permutation traffic pattern. It is widely considered in the literature that under the permutation traffic pattern, the source-destination pairs are matched at random in a way that the destination sequence is a permutation of the source sequence, e.g., [1-3].
My question is as follows.
A general permutation may have fixed points. An element is called a fixed point of a permutation, if it is mapped to itself under the permutation. E.g., for the permutation :(1,2,3)->(2,1,3), 3 is a fixed point of this permutation.
Since a permutation may have fixed points, the permutation traffic pattern literally allows a source to select itself as the destination of the traffic flow originated from this node. It is notable, however, it does not make sense that a node transmits to itself in a wireless network. Besides, in this case, the throughput capacity would become infinite, since the transmission to oneself does not use the wireless medium.
Therefore, the term permutation traffic pattern might be misleading in my understanding. And I am wondering whether this misleading term can be replaced by the derangement traffic pattern. Since a derangement is a permutation without any fixed point, the confusing case of talking to oneself can be avoided.
Does anyone agree with me or have a better understanding/explanation on the permutation traffic pattern?
 P. Gupta and P. R. Kumar. The capacity of wireless networks. IEEE Transactions on Information Theory, 46(2):388–404, March 2000.
 Niesen, Urs, Piyush Gupta, and Devavrat Shah. "On capacity scaling in arbitrary wireless networks." Information Theory, IEEE Transactions on 55, no. 9 (2009): 3959-3982.
 Garetto, Michele, Paolo Giaccone, and Emilio Leonardi. "Capacity scaling in ad hoc networks with heterogeneous mobile nodes: The subcritical regime." IEEE/ACM Transactions on Networking (TON) 17, no. 6 (2009): 1888-1901.
I'm working in modifying existed protocol (OLSR) and I want to add this modification into OPNET modeler 14.5 to check if it works well or not.
References are better for helping and also any other tools