graph

问题 The dataset is included the network matrix and attribute data frame. Network dataset has 3 data set itself, that I just want to work on PrinFull dataset and also just PRIN attribute data. my data is uploaded in this two link below. I added all attribute on my data set. https://drive.google.com/file/d/1MZCdeAZF0joIQLwVeoVXmKpf7r8IJ2wq/view?usp=sharing https://drive.google.com/file/d/1I96BAUo8TjJMWCWpn_SIhp54snfZ0Bd5/view?usp=sharing I want to plot my community detection algorithm, the code is

问题 I've been looking at the Gremlin graph language, and it appears very powerful. However, whilst running through what it can do to evaluate it against requirements, I came across a case that I can't seem to complete. Assuming Gremlin is started, and using its example database: gremlin> g = TinkerGraphFactory.createTinkerGraph() ... gremlin> g.V.out('knows') ==>v[2] ==>v[4] So this shows vertices that have an edge of 'knows'. However, I want to find vertices that do not have edges of 'knows'.

问题 now, i'm studying tensorflow. but, i can't draw dot graph using tensorboard. if i have sample data for training, like that train_X = numpy.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779]) train_Y = numpy.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366]) i want to show scatter plots using tensorboard. i know "import matplotlib.pyplot as plt" can do that. but i can just use console (putty). so can't use this method. can i see dot graph, like scatter plots using tensorboard. can anyone

问题 now, i'm studying tensorflow. but, i can't draw dot graph using tensorboard. if i have sample data for training, like that train_X = numpy.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779]) train_Y = numpy.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366]) i want to show scatter plots using tensorboard. i know "import matplotlib.pyplot as plt" can do that. but i can just use console (putty). so can't use this method. can i see dot graph, like scatter plots using tensorboard. can anyone

问题 now, i'm studying tensorflow. but, i can't draw dot graph using tensorboard. if i have sample data for training, like that train_X = numpy.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779]) train_Y = numpy.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366]) i want to show scatter plots using tensorboard. i know "import matplotlib.pyplot as plt" can do that. but i can just use console (putty). so can't use this method. can i see dot graph, like scatter plots using tensorboard. can anyone

问题 I would like to create a combination bar plot where within data1 and data2 are stacked while between data1 and data2 are grouped in base R graphics. Something like this in arrangement: data1 <- matrix(c(1:5, rep(1,5), rep(4,5)), ncol=5) data2 <- matrix(c(2:6, rep(2,5), rep(3,5)), ncol=5) # stacked bar barplot(data1) #grouped var barplot(data1, beside=TRUE) Any idea how can I do this? I know we can do this using different plots or facets but I would like to present them side by side in a

问题 I am working with enron email dataset and I am trying to remove email addresses that don't have "@enron.com" (i.e. I would like to have enron emails only). When I tried to delete those addresses without @enron.com, some emails just got skipped for some reasons. A small graph is shown below where vertices are email address. This is gml format: Creator "igraph version 0.7 Sun Mar 29 20:15:45 2015" Version 1 graph [ directed 1 node [ id 0 label "csutter@enron.com" ] node [ id 1 label "steve

问题 I am trying to grasp how the trie and such in immutability is implemented, as relates to immutability in JS. I understand how there is supposed to be significant structural sharing. My question is say you have a graph sort of structure like this: a -- b | c | d -- h | e -- i -- l | f -- j -- m | g -- k -- n So then you add an x to the system. I'll try it two different ways: a -- b | c | d -- h -- x | e -- i -- l | f -- j -- m | g -- k -- n That one is just added as a leaf node. a -- b | c | d

问题 I'm confused about '=' operator, and tf.identity() , I thought '=' is to just make a reference of the tensor, and identity is to make a copy, e.g., with ref = x ref = ref*0 sess.run(x) I will get x all been set to 0 element-wise, and with copy = tf.identity(x) copy = ref*0 sess.run(x) x would not be changed, since identity make copy, not a reference, but with experiment, '=' also make a copy and x is not set to 0, so what's the difference? 回答1: The difference is only in tensorlfow graph

问题 Directed Graph (|V|=a, |E|=b) is given. each vertexes has specific weight. we want for each vertex (1..a) find a vertex with maximum weight that can be reachable from that vertex. Update 1: one nice answer is prepare by @Paul in O(b + a log a). but I search for O(a + b) algorithms, if any? Is there any different efficient or fastest any other ways for doing it? 回答1: Yes, it's possible to modify Tarjan's SCC algorithm to solve this problem in linear time. Tarjan's algorithm uses two node