Effective Semisupervised Community Detection Using Negative Information

<div>Testing the accuracy of our algorithm with the various parameter <svg height="9.7821pt" id="M206" style="vertical-align:-3.57194pt" version="1.1" viewbox="-0.0498162 -6.21016 7.64704 9.7821" width="7.64704pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M538 96L524 119C492 88 454 63 446 63C439 63 434 70 440 101C463 223 491 341 518 448H508L433 422L401 276C355 192 240 56 188 56C163 56 154 89 163 133C184 233 207 338 235 443L230 448L152 424L58 17C40 -60 23 -143 23 -185C23 -241 42 -261 63 -261C92 -261 117 -241 125 -227L124 -221C109 -209 89 -165 89 -103C89 -55 96 -13 105 26H107C121 -3 134 -12 151 -12C172 -12 194 -5 221 16C279 61 330 124 384 194H386C381 159 377 141 368 105C343 3 363 -12 383 -12C416 -12 486 35 538 96Z" id="g113-234"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="555" vert-adv-y="555"></glyph.data></g></svg>. Each point in the graph represents the average over 100 runs on randomly generated LFR benchmark networks with the given parameters.</div>

Mathematical Problems in Engineering

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Figure 2

Figure 2: Effective Semisupervised Community Detection Using Negative Information 