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ChronoSense / concept_network_visualization.html
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 <html>
<head>
<meta charset="utf-8">
<script>function neighbourhoodHighlight(params) {
// console.log("in nieghbourhoodhighlight");
allNodes = nodes.get({ returnType: "Object" });
// originalNodes = JSON.parse(JSON.stringify(allNodes));
// if something is selected:
if (params.nodes.length > 0) {
highlightActive = true;
var i, j;
var selectedNode = params.nodes[0];
var degrees = 2;
// mark all nodes as hard to read.
for (let nodeId in allNodes) {
// nodeColors[nodeId] = allNodes[nodeId].color;
allNodes[nodeId].color = "rgba(200,200,200,0.5)";
if (allNodes[nodeId].hiddenLabel === undefined) {
allNodes[nodeId].hiddenLabel = allNodes[nodeId].label;
allNodes[nodeId].label = undefined;
}
}
var connectedNodes = network.getConnectedNodes(selectedNode);
var allConnectedNodes = [];
// get the second degree nodes
for (i = 1; i < degrees; i++) {
for (j = 0; j < connectedNodes.length; j++) {
allConnectedNodes = allConnectedNodes.concat(
network.getConnectedNodes(connectedNodes[j])
);
}
}
// all second degree nodes get a different color and their label back
for (i = 0; i < allConnectedNodes.length; i++) {
// allNodes[allConnectedNodes[i]].color = "pink";
allNodes[allConnectedNodes[i]].color = "rgba(150,150,150,0.75)";
if (allNodes[allConnectedNodes[i]].hiddenLabel !== undefined) {
allNodes[allConnectedNodes[i]].label =
allNodes[allConnectedNodes[i]].hiddenLabel;
allNodes[allConnectedNodes[i]].hiddenLabel = undefined;
}
}
// all first degree nodes get their own color and their label back
for (i = 0; i < connectedNodes.length; i++) {
// allNodes[connectedNodes[i]].color = undefined;
allNodes[connectedNodes[i]].color = nodeColors[connectedNodes[i]];
if (allNodes[connectedNodes[i]].hiddenLabel !== undefined) {
allNodes[connectedNodes[i]].label =
allNodes[connectedNodes[i]].hiddenLabel;
allNodes[connectedNodes[i]].hiddenLabel = undefined;
}
}
// the main node gets its own color and its label back.
// allNodes[selectedNode].color = undefined;
allNodes[selectedNode].color = nodeColors[selectedNode];
if (allNodes[selectedNode].hiddenLabel !== undefined) {
allNodes[selectedNode].label = allNodes[selectedNode].hiddenLabel;
allNodes[selectedNode].hiddenLabel = undefined;
}
} else if (highlightActive === true) {
// console.log("highlightActive was true");
// reset all nodes
for (let nodeId in allNodes) {
// allNodes[nodeId].color = "purple";
allNodes[nodeId].color = nodeColors[nodeId];
// delete allNodes[nodeId].color;
if (allNodes[nodeId].hiddenLabel !== undefined) {
allNodes[nodeId].label = allNodes[nodeId].hiddenLabel;
allNodes[nodeId].hiddenLabel = undefined;
}
}
highlightActive = false;
}
// transform the object into an array
var updateArray = [];
if (params.nodes.length > 0) {
for (let nodeId in allNodes) {
if (allNodes.hasOwnProperty(nodeId)) {
// console.log(allNodes[nodeId]);
updateArray.push(allNodes[nodeId]);
}
}
nodes.update(updateArray);
} else {
// console.log("Nothing was selected");
for (let nodeId in allNodes) {
if (allNodes.hasOwnProperty(nodeId)) {
// console.log(allNodes[nodeId]);
// allNodes[nodeId].color = {};
updateArray.push(allNodes[nodeId]);
}
}
nodes.update(updateArray);
}
}
function filterHighlight(params) {
allNodes = nodes.get({ returnType: "Object" });
// if something is selected:
if (params.nodes.length > 0) {
filterActive = true;
let selectedNodes = params.nodes;
// hiding all nodes and saving the label
for (let nodeId in allNodes) {
allNodes[nodeId].hidden = true;
if (allNodes[nodeId].savedLabel === undefined) {
allNodes[nodeId].savedLabel = allNodes[nodeId].label;
allNodes[nodeId].label = undefined;
}
}
for (let i=0; i < selectedNodes.length; i++) {
allNodes[selectedNodes[i]].hidden = false;
if (allNodes[selectedNodes[i]].savedLabel !== undefined) {
allNodes[selectedNodes[i]].label = allNodes[selectedNodes[i]].savedLabel;
allNodes[selectedNodes[i]].savedLabel = undefined;
}
}
} else if (filterActive === true) {
// reset all nodes
for (let nodeId in allNodes) {
allNodes[nodeId].hidden = false;
if (allNodes[nodeId].savedLabel !== undefined) {
allNodes[nodeId].label = allNodes[nodeId].savedLabel;
allNodes[nodeId].savedLabel = undefined;
}
}
filterActive = false;
}
// transform the object into an array
var updateArray = [];
if (params.nodes.length > 0) {
for (let nodeId in allNodes) {
if (allNodes.hasOwnProperty(nodeId)) {
updateArray.push(allNodes[nodeId]);
}
}
nodes.update(updateArray);
} else {
for (let nodeId in allNodes) {
if (allNodes.hasOwnProperty(nodeId)) {
updateArray.push(allNodes[nodeId]);
}
}
nodes.update(updateArray);
}
}
function selectNode(nodes) {
network.selectNodes(nodes);
neighbourhoodHighlight({ nodes: nodes });
return nodes;
}
function selectNodes(nodes) {
network.selectNodes(nodes);
filterHighlight({nodes: nodes});
return nodes;
}
function highlightFilter(filter) {
let selectedNodes = []
let selectedProp = filter['property']
if (filter['item'] === 'node') {
let allNodes = nodes.get({ returnType: "Object" });
for (let nodeId in allNodes) {
if (allNodes[nodeId][selectedProp] && filter['value'].includes((allNodes[nodeId][selectedProp]).toString())) {
selectedNodes.push(nodeId)
}
}
}
else if (filter['item'] === 'edge'){
let allEdges = edges.get({returnType: 'object'});
// check if the selected property exists for selected edge and select the nodes connected to the edge
for (let edge in allEdges) {
if (allEdges[edge][selectedProp] && filter['value'].includes((allEdges[edge][selectedProp]).toString())) {
selectedNodes.push(allEdges[edge]['from'])
selectedNodes.push(allEdges[edge]['to'])
}
}
}
selectNodes(selectedNodes)
}</script>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/vis-network/9.1.2/dist/dist/vis-network.min.css" integrity="sha512-WgxfT5LWjfszlPHXRmBWHkV2eceiWTOBvrKCNbdgDYTHrT2AeLCGbF4sZlZw3UMN3WtL0tGUoIAKsu8mllg/XA==" crossorigin="anonymous" referrerpolicy="no-referrer" />
<script src="https://cdnjs.cloudflare.com/ajax/libs/vis-network/9.1.2/dist/vis-network.min.js" integrity="sha512-LnvoEWDFrqGHlHmDD2101OrLcbsfkrzoSpvtSQtxK3RMnRV0eOkhhBN2dXHKRrUU8p2DGRTk35n4O8nWSVe1mQ==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
<center>
<h1>ChronoSense Konsept A�� (Metriklerle)</h1>
</center>
<!-- <link rel="stylesheet" href="../node_modules/vis/dist/vis.min.css" type="text/css" />
<script type="text/javascript" src="../node_modules/vis/dist/vis.js"> </script>-->
<link
href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta3/dist/css/bootstrap.min.css"
rel="stylesheet"
integrity="sha384-eOJMYsd53ii+scO/bJGFsiCZc+5NDVN2yr8+0RDqr0Ql0h+rP48ckxlpbzKgwra6"
crossorigin="anonymous"
/>
<script
src="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta3/dist/js/bootstrap.bundle.min.js"
integrity="sha384-JEW9xMcG8R+pH31jmWH6WWP0WintQrMb4s7ZOdauHnUtxwoG2vI5DkLtS3qm9Ekf"
crossorigin="anonymous"
></script>
<center>
<h1>ChronoSense Konsept A�� (Metriklerle)</h1>
</center>
<style type="text/css">
#mynetwork {
width: 100%;
height: 800px;
background-color: #ffffff;
border: 1px solid lightgray;
position: relative;
float: left;
}
#config {
float: left;
width: 400px;
height: 600px;
}
</style>
</head>
<body>
<div class="card" style="width: 100%">
<div id="mynetwork" class="card-body"></div>
</div>
<div id="config"></div>
<script type="text/javascript">
// initialize global variables.
var edges;
var nodes;
var allNodes;
var allEdges;
var nodeColors;
var originalNodes;
var network;
var container;
var options, data;
var filter = {
item : '',
property : '',
value : []
};
// This method is responsible for drawing the graph, returns the drawn network
function drawGraph() {
var container = document.getElementById('mynetwork');
// parsing and collecting nodes and edges from the python
nodes = new vis.DataSet([{"color": "#ff7f0e", "id": "b8566bb8-f043-45d0-8442-c8f3e729a626", "label": "ai", "shape": "dot", "size": 40.0, "title": "ID: b8566bb8-f043-45d0-8442-c8f3e729a626\u003cbr\u003eName: ai\u003cbr\u003edegree_centrality: 0.300\u003cbr\u003ecommunity_id: 1"}, {"color": "#2ca02c", "id": "acdb0052-9fb5-4a61-8ce3-4fa9188ccd68", "label": "unsupervised learning: finding", "shape": "dot", "size": 40.0, "title": "ID: acdb0052-9fb5-4a61-8ce3-4fa9188ccd68\u003cbr\u003eName: unsupervised learning: finding\u003cbr\u003edegree_centrality: 0.300\u003cbr\u003ecommunity_id: 2"}, {"color": "#2ca02c", "id": "c9a071e5-358b-460f-897d-5a0d68b4dc91", "label": "reinforcement learning", "shape": "dot", "size": 40.0, "title": "ID: c9a071e5-358b-460f-897d-5a0d68b4dc91\u003cbr\u003eName: reinforcement learning\u003cbr\u003edegree_centrality: 0.300\u003cbr\u003ecommunity_id: 2"}, {"color": "#d62728", "id": "8bcb0007-453a-45a8-b0f5-ccb49fc963be", "label": "deep learning", "shape": "dot", "size": 10, "title": "ID: 8bcb0007-453a-45a8-b0f5-ccb49fc963be\u003cbr\u003eName: deep learning\u003cbr\u003edegree_centrality: 0.000\u003cbr\u003ecommunity_id: 3"}, {"color": "#1f77b4", "id": "544a779d-f9b6-4720-bfdf-80a26574d819", "label": "nlp", "shape": "dot", "size": 20.0, "title": "ID: 544a779d-f9b6-4720-bfdf-80a26574d819\u003cbr\u003eName: nlp\u003cbr\u003edegree_centrality: 0.100\u003cbr\u003ecommunity_id: 0"}, {"color": "#ff7f0e", "id": "1b3a4eb6-a80f-4098-b98e-2ca50ecbdbc6", "label": "chatbots", "shape": "dot", "size": 30.0, "title": "ID: 1b3a4eb6-a80f-4098-b98e-2ca50ecbdbc6\u003cbr\u003eName: chatbots\u003cbr\u003edegree_centrality: 0.200\u003cbr\u003ecommunity_id: 1"}, {"color": "#2ca02c", "id": "ffec4610-96c3-4a0f-a592-573143619a30", "label": "supervised learning", "shape": "dot", "size": 40.0, "title": "ID: ffec4610-96c3-4a0f-a592-573143619a30\u003cbr\u003eName: supervised learning\u003cbr\u003edegree_centrality: 0.300\u003cbr\u003ecommunity_id: 2"}, {"color": "#2ca02c", "id": "c7b69b48-9fea-45de-868d-27f935a7b2b7", "label": "labeled data unsupervised learning", "shape": "dot", "size": 40.0, "title": "ID: c7b69b48-9fea-45de-868d-27f935a7b2b7\u003cbr\u003eName: labeled data unsupervised learning\u003cbr\u003edegree_centrality: 0.300\u003cbr\u003ecommunity_id: 2"}, {"color": "#1f77b4", "id": "18f1cc03-9cfc-40c8-aa86-279a700a7f58", "label": "this approach", "shape": "dot", "size": 20.0, "title": "ID: 18f1cc03-9cfc-40c8-aa86-279a700a7f58\u003cbr\u003eName: this approach\u003cbr\u003edegree_centrality: 0.100\u003cbr\u003ecommunity_id: 0"}, {"color": "#ff7f0e", "id": "78b888f4-c0bf-492e-b514-3da1f628797d", "label": "gpt-4", "shape": "dot", "size": 30.0, "title": "ID: 78b888f4-c0bf-492e-b514-3da1f628797d\u003cbr\u003eName: gpt-4\u003cbr\u003edegree_centrality: 0.200\u003cbr\u003ecommunity_id: 1"}, {"color": "#ff7f0e", "id": "903e5742-9937-42c1-917d-ea7ff7ac449e", "label": "these models", "shape": "dot", "size": 20.0, "title": "ID: 903e5742-9937-42c1-917d-ea7ff7ac449e\u003cbr\u003eName: these models\u003cbr\u003edegree_centrality: 0.100\u003cbr\u003ecommunity_id: 1"}]);
edges = new vis.DataSet([{"color": "#9370DB", "from": "b8566bb8-f043-45d0-8442-c8f3e729a626", "title": "Type: combined\u003cbr\u003eRelation: RELATED_TO\u003cbr\u003eSimilarity: 0.648", "to": "1b3a4eb6-a80f-4098-b98e-2ca50ecbdbc6", "value": 0.647527813911438}, {"color": "#9370DB", "from": "b8566bb8-f043-45d0-8442-c8f3e729a626", "title": "Type: combined\u003cbr\u003eRelation: RELATED_TO\u003cbr\u003eSimilarity: 0.648", "to": "78b888f4-c0bf-492e-b514-3da1f628797d", "value": 0.647527813911438}, {"color": "#4682B4", "from": "b8566bb8-f043-45d0-8442-c8f3e729a626", "title": "Type: similarity\u003cbr\u003eSimilarity: 0.627", "to": "903e5742-9937-42c1-917d-ea7ff7ac449e", "value": 0.6268218755722046}, {"color": "#FF6347", "from": "acdb0052-9fb5-4a61-8ce3-4fa9188ccd68", "title": "Type: extracted\u003cbr\u003eRelation: RELATED_TO", "to": "c9a071e5-358b-460f-897d-5a0d68b4dc91", "value": 0.8}, {"color": "#FF6347", "from": "acdb0052-9fb5-4a61-8ce3-4fa9188ccd68", "title": "Type: extracted\u003cbr\u003eRelation: RELATED_TO", "to": "ffec4610-96c3-4a0f-a592-573143619a30", "value": 0.8}, {"color": "#FF6347", "from": "acdb0052-9fb5-4a61-8ce3-4fa9188ccd68", "title": "Type: extracted\u003cbr\u003eRelation: RELATED_TO", "to": "c7b69b48-9fea-45de-868d-27f935a7b2b7", "value": 0.8}, {"color": "#FF6347", "from": "c9a071e5-358b-460f-897d-5a0d68b4dc91", "title": "Type: extracted\u003cbr\u003eRelation: RELATED_TO", "to": "ffec4610-96c3-4a0f-a592-573143619a30", "value": 0.8}, {"color": "#FF6347", "from": "c9a071e5-358b-460f-897d-5a0d68b4dc91", "title": "Type: extracted\u003cbr\u003eRelation: RELATED_TO", "to": "c7b69b48-9fea-45de-868d-27f935a7b2b7", "value": 0.8}, {"color": "#FF6347", "from": "544a779d-f9b6-4720-bfdf-80a26574d819", "title": "Type: extracted\u003cbr\u003eRelation: RELATED_TO", "to": "18f1cc03-9cfc-40c8-aa86-279a700a7f58", "value": 0.8}, {"color": "#FF6347", "from": "1b3a4eb6-a80f-4098-b98e-2ca50ecbdbc6", "title": "Type: extracted\u003cbr\u003eRelation: RELATED_TO", "to": "78b888f4-c0bf-492e-b514-3da1f628797d", "value": 0.8}, {"color": "#FF6347", "from": "ffec4610-96c3-4a0f-a592-573143619a30", "title": "Type: extracted\u003cbr\u003eRelation: RELATED_TO", "to": "c7b69b48-9fea-45de-868d-27f935a7b2b7", "value": 0.8}]);
nodeColors = {};
allNodes = nodes.get({ returnType: "Object" });
for (nodeId in allNodes) {
nodeColors[nodeId] = allNodes[nodeId].color;
}
allEdges = edges.get({ returnType: "Object" });
// adding nodes and edges to the graph
data = {nodes: nodes, edges: edges};
var options = {
"configure": {
"enabled": true,
"filter": [
"physics",
"nodes",
"edges"
]
},
"edges": {
"color": {
"inherit": true
},
"smooth": {
"enabled": true,
"type": "dynamic"
}
},
"interaction": {
"dragNodes": true,
"hideEdgesOnDrag": false,
"hideNodesOnDrag": false
},
"physics": {
"barnesHut": {
"avoidOverlap": 0,
"centralGravity": 0.1,
"damping": 0.09,
"gravitationalConstant": -8000,
"springConstant": 0.005,
"springLength": 150
},
"enabled": true,
"stabilization": {
"enabled": true,
"fit": true,
"iterations": 1000,
"onlyDynamicEdges": false,
"updateInterval": 50
}
}
};
// if this network requires displaying the configure window,
// put it in its div
options.configure["container"] = document.getElementById("config");
network = new vis.Network(container, data, options);
return network;
}
drawGraph();
</script>
</body>
</html>