graph-dynamics lib
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graph-dynamics lib
Please create the network of computer science or something small so we are able to make experiments quickly in something relatively small
Once the MongoDBHandlers is migrated please include in the aggregation
/MongoDBHandlers/AggreagateMongoDB.ipynb
Below the aggregation markup the identifier of the transaction, since that will allow us to create bipartegraph if necessary.
Modify the TX model so that the activity changes every time, keep the number of neighbors constant. Preferably assign the activity as a function the amount of money.
BITCOIN. Bitcoin is a decentralized digital currency and payment
system. This dataset consists of all payments made up to October
19, 2014 [11]. Nodes in the network correspond to Bitcoin addresses,
and an individual may have several addresses. An edge
(u, v, t) signifies that bitcoin was transferred from address u to address
v at time t.
The gains of the fast
algorithm are the largest for BITCOIN, which is due to some pairs
of nodes having many edges between them and also participating
in many triangles
Cycles in BITCOIN. Of the eight 3-edge triangle motifs, M2,4 and
M3,5 are cyclic, i.e., the target of each edge serves as the source
of another edge. We observe in Fig. 8 that the fraction of triangles
that are cyclic is much higher in BITCOIN compared to any
other dataset. This can be attributed to the transactional nature of
BITCOIN where the total amount of bitcoin is limited. Since remittance
(outgoing edges) is typically associated wit
edges constructed from the transactions: a transaction with 2 inputs and 3 outputs results in 6 edges (all possible combinations), an edge may appear multiple times, with the corresponding transaction IDs http://www.vo.elte.hu/bitcoin/downloads.htm#database
let there be trade inferring the dynamics of the time-varying bitcoin transaction network
Please check in the drive the papers relating hierarchies and centralities.
Create graphs using the code:
And try to see if the nodes embeddings recover the different levels of the hierarchy. If not, try to modify the random walks until they do
Time Series of:
No need to use the abstract classes for now
For start use:
https://snap.stanford.edu/data/cit-HepTh.html
During the training phase, we observe a certain fraction of the nodes and all their labels.
The task is to predict the labels for the remaining nodes.
The node features representations are input to a one-vs-rest logist regression classifier with L2 regularization.
The theory regarding the finite process is located in:
https://drive.google.com/open?id=0B5P9ajMXC2Y0ZFV1QlYyNU51Z1E
The simple beta process is just an example that serve as a motivation for the thing. In principle you should create something similar to:
Fit exponential and polynomials to the centrality evolutions.
In order to evaluate the fit use the function defined in the following paper:
https://drive.google.com/open?id=0B5P9ajMXC2Y0eDdFYmtxZXdJVHc
There you can see the variance which is simply the error penalized with the number of parameters
Evolution must incorporate the N variables corresponding to the time dependance of the edges:
https://github.com/ernaneluis/graph-dynamics/blob/master/graph_dynamics/dynamics/PittWalker.py#L87
recreate the plots shown:
Obtain binary matrix from Beta process:
Take a look at:
https://drive.google.com/open?id=0B1WmdewksxI9WUE0X01feU9OQ2s
Create a 3D plot for the graph time series, where the Y dimension is time and X and Z correspond to the position of the nodes.
Similar to https://matplotlib.org/examples/mplot3d/bars3d_demo.html
only that instead of a bar plot, you have a graph from the graph time series
Please locate the slowest functions or methods in
https://github.com/ernaneluis/graph-dynamics/blob/master/tests/dynamicsTest.py#L21
one graph in which the edges have attributes
when they where created (and if possible when they where destroyed)
that way instead of a list of graph
we have just one graph
In Link prediction, we are given a network with a certain fraction of edges removed,
and we would like to predict these missing edges. We generate the labeled data set as follows:
To obtain positive examples, we remove 50% of the edges chosen randomly from the network while ensuring that the residual network obtained after the edge removals is connected,
DO THE PREDICTION FOR THE FOLLOWING DATA SET:
be a modular function of dynamics
Please define a class for the bitcoin models, we need t simply to finish this class:
https://github.com/ernaneluis/graph-dynamics/blob/master/graph_dynamics/networks/generative_graphs.py#L16
Called simply bitcoin graph or ernane graph since is not goin to be generate but to define a graph instead
be need a framework like
https://github.com/ernaneluis/graph-dynamics/blob/master/tests/dynamicsTest.py#L35
So we can define a graph and then evolve it. This will require an abstract class called Graphs or something like that, but I will write it down after you work on the particular example
This function should have as parameters the time window required for the evolution.
Train the big clam per (community number ) snapshot for the real transaction network, this should be a function applied to dynamics.
use the bigclam to find out the activz rate to be the input parameter of the perra graph
and return a times series of the bigclam edges(activity rate) matrix to be a dynamic function
We need to define a dynamical class and graph for the simple Parra Model
https://drive.google.com/open?id=0B5P9ajMXC2Y0NTd6QkhMOGFvWDQ
This should be a simple version of the already existing Tx Dynamics
This will require a detailed series of edges from the graph series in order to perform a posterior transformation or to include this directly in the dynamics
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