In [2]:
import networkx as nx
import numpy as np
from snpp.utils.matrix import load_sparse_csr
from snpp.cores.correlation_clustering import sampling_wrapper, \
agglomerative
dataset = 'wiki'
In [3]:
g = nx.from_scipy_sparse_matrix(load_sparse_csr('data/{}.npz'.format(dataset)),
create_using=nx.Graph(), edge_attribute='sign')
In [4]:
print('number of nodes {}'.format(g.number_of_nodes()))
number of nodes 11259
In [5]:
sample_size = 2000
threshold = -0.7
In [6]:
clus = sampling_wrapper(g, agglomerative,
threshold=threshold,
sample_size=sample_size)
0it [00:00, ?it/s]
sample_size 2000
928it [01:49, 61.23it/s]
0%| | 27/9259 [00:00<00:34, 264.95it/s]
partition on the samples produces 1073 clusters
remainign nodes to assign clusters 9259
100%|██████████| 9259/9259 [00:42<00:00, 217.54it/s]
0it [00:00, ?it/s]
singleton_nodes (3173)
113it [00:06, 2.16it/s]
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{4864}, 2189: {4865}, 2190: {4869}, 2191: {4870}, 2192: {4872}, 2193: {4876}, 2194: {4878}, 2195: {4880}, 2196: {4881}, 2197: {4883}, 2198: {4884}, 2199: {4887}, 2200: {4889}, 2201: {9007}, 2202: {4894}, 2203: {4895}, 2204: {4896}, 2205: {4897}, 2206: {4900}, 2207: {4903}, 2208: {4907}, 2209: {4908}, 2210: {4911}, 2211: {4915}, 2212: {4920}, 2213: {9012}, 2214: {4924}, 2215: {9013}, 2216: {4937}, 2217: {4938}, 2218: {4944, 972}, 2219: {4945}, 2220: {4947}, 2221: {4950}, 2222: {4921}, 2223: {4955}, 2224: {4957}, 2225: {4961}, 2226: {4969}, 2227: {4974}, 2228: {4975}, 2229: {4983}, 2230: {4984}, 2231: {4985}, 2232: {4987}, 2233: {4991}, 2234: {4994}, 2235: {4996}, 2236: {4998}, 2237: {4999}, 2238: {5001}, 2239: {5003}, 2240: {5004}, 2241: {5005}, 2242: {5006}, 2243: {5011}, 2244: {5017}, 2245: {5018}, 2246: {5019}, 2247: {5020}, 2248: {5022}, 2249: {5024}, 2250: {5027}, 2251: {5029}, 2252: {10396}, 2253: {5035}, 2254: {5036}, 2255: {5041}, 2256: {10398}, 2257: {5050}, 2258: {5051}, 2259: {5068}, 2260: {5069}, 2261: {5072}, 2262: {5077}, 2263: {5078}, 2264: {5081}, 2265: {9039}, 2266: {5094}, 2267: {5096}, 2268: {5097}, 2269: {5104}, 2270: {5105}, 2271: {5106}, 2272: {5107}, 2273: {9044}, 2274: {5122}, 2275: {5124}, 2276: {5125}, 2277: {5129}, 2278: {5132}, 2279: {5139}, 2280: {5141}, 2281: {5144}, 2282: {5146}, 2283: {5149}, 2284: {5151}, 2285: {5152}, 2286: {5153}, 2287: {5155}, 2288: {5161}, 2289: {5162}, 2290: {5166}, 2291: {5170}, 2292: {5172}, 2293: {5174}, 2294: {5178}, 2295: {5179}, 2296: {5190}, 2297: {5193}, 2298: {5197}, 2299: {5208}, 2300: {5215}, 2301: {5218}, 2302: {9721}, 2303: {5220}, 2304: {5222}, 2305: {5225}, 2306: {5233}, 2307: {5234}, 2308: {5238}, 2309: {5239}, 2310: {5242}, 2311: {5256}, 2312: {10434}, 2313: {5262}, 2314: {5271}, 2315: {5280}, 2316: {5282}, 2317: {7808, 5285}, 2318: {5289}, 2319: {5295}, 2320: {5300}, 2321: {5306}, 2322: {5307}, 2323: {5310}, 2324: {5311}, 2325: {5319}, 2326: {5327}, 2327: {5332}, 2328: {5333}, 2329: {5340}, 2330: {5347}, 2331: {5348}, 2332: {5355}, 2333: {5356}, 2334: {5357}, 2335: {5358}, 2336: {5359}, 2337: {5360}, 2338: {5363}, 2339: {5366}, 2340: {5368}, 2341: {5386}, 2342: {5387}, 2343: {5394}, 2344: {5396}, 2345: {5399}, 2346: {5401}, 2347: {5402}, 2348: {10458}, 2349: {5417}, 2350: {5428}, 2351: {5433}, 2352: {5436}, 2353: {5447}, 2354: {5450}, 2355: {7735}, 2356: {5452}, 2357: {5454}, 2358: {5455}, 2359: {5456}, 2360: {5457}, 2361: {5459}, 2362: {5460}, 2363: {5465}, 2364: {5466}, 2365: {5467}, 2366: {5468}, 2367: {5471}, 2368: {5474}, 2369: {5482}, 2370: {914}, 2371: {5486}, 2372: {6664, 5492}, 2373: {5493}, 2374: {5499}, 2375: {5503}, 2376: {5515}, 2377: {5517}, 2378: {5519}, 2379: {5521}, 2380: {5524}, 2381: {9114}, 2382: {5538}, 2383: {5539}, 2384: {5552}, 2385: {5554}, 2386: {5557}, 2387: {5558}, 2388: {5559}, 2389: {5561, 6205}, 2390: {5563}, 2391: {5564}, 2392: {5567}, 2393: {928}, 2394: {5572}, 2395: {10486}, 2396: {5575}, 2397: {5578}, 2398: {5580}, 2399: {931}, 2400: {5591}, 2401: {5599}, 2402: {5603}, 2403: {934}, 2404: {5610}, 2405: {5613}, 2406: {5616}, 2407: {936}, 2408: {937}, 2409: {5628}, 2410: {5630}, 2411: {5639}, 2412: {5641}, 2413: {5646}, 2414: {5662}, 2415: {5664}, 2416: {5666}, 2417: {5670}, 2418: {5672}, 2419: {5674}, 2420: {8918}, 2421: {5681}, 2422: {5685}, 2423: {5687}, 2424: {462}, 2425: {5689}, 2426: {5690}, 2427: {5694}, 2428: {5696}, 2429: {5697}, 2430: {5699}, 2431: {5700}, 2432: {5701}, 2433: {5702}, 2434: {5703}, 2435: {5704}, 2436: {2316}, 2437: {5711}, 2438: {5717}, 2439: {3191}, 2440: {5724}, 2441: {7141, 5727}, 2442: {5735}, 2443: {5736}, 2444: {5737}, 2445: {5741}, 2446: {5743}, 2447: {1555}, 2448: {5745}, 2449: {5748}, 2450: {5752}, 2451: {5753}, 2452: {5754}, 2453: {5756}, 2454: {5758}, 2455: {5761}, 2456: {10022}, 2457: {5775}, 2458: {5781}, 2459: {5782}, 2460: {5788}, 2461: {965}, 2462: {5800}, 2463: {5808}, 2464: {5812}, 2465: {5815}, 2466: {5818}, 2467: {5820}, 2468: {5823}, 2469: {5825}, 2470: {5828}, 2471: {5838}, 2472: {5845}, 2473: {5848}, 2474: {5850}, 2475: {975}, 2476: {5856}, 2477: {5858}, 2478: {5860}, 2479: {5864}, 2480: {5865}, 2481: {5866}, 2482: {6439}, 2483: {7015}, 2484: {5878}, 2485: {980}, 2486: {5883}, 2487: {5884}, 2488: {5888}, 2489: {5889}, 2490: {5890}, 2491: {5900}, 2492: {5903}, 2493: {2350}, 2494: {9177}, 2495: {5917}, 2496: {5919}, 2497: {5922}, 2498: {5083}, 2499: {5926}, 2500: {5934}, 2501: {5939}, 2502: {5949}, 2503: {5950}, 2504: {8823}, 2505: {5953}, 2506: {5957}, 2507: {5961}, 2508: {5964}, 2509: {5969}, 2510: {5974}, 2511: {5976}, 2512: {5983}, 2513: {5987}, 2514: {5988}, 2515: {5991}, 2516: {5996}, 2517: {5999}, 2518: {6002}, 2519: {11111}, 2520: {6008}, 2521: {6011}, 2522: {6014}, 2523: {6016}, 2524: {6029}, 2525: {6032}, 2526: {6033}, 2527: {6037}, 2528: {6041}, 2529: {6044}, 2530: {6056}, 2531: {6067}, 2532: {6072}, 2533: {6077}, 2534: {6078}, 2535: {6084}, 2536: {6087}, 2537: {6088}, 2538: {6089}, 2539: {6091}, 2540: {6100}, 2541: {6101}, 2542: {6103}, 2543: {1457}, 2544: {6123}, 2545: {6129}, 2546: {6131}, 2547: {6133}, 2548: {6137}, 2549: {6146}, 2550: {6154}, 2551: {6156}, 2552: {6158}, 2553: {6160}, 2554: {6165}, 2555: {6167}, 2556: {6169}, 2557: {6171}, 2558: {6182}, 2559: {6190}, 2560: {6191}, 2561: {6194}, 2562: {6195}, 2563: {6197}, 2564: {479}, 2565: {1843}, 2566: {2399}, 2567: {6204}, 2568: {6206}, 2569: {6208}, 2570: {6215}, 2571: {6223}, 2572: {6225}, 2573: {6227}, 2574: {6230}, 2575: {6237}, 2576: {6244}, 2577: {6245}, 2578: {6246}, 2579: {6250}, 2580: {1569}, 2581: {6252}, 2582: {6253}, 2583: {6254}, 2584: {6262}, 2585: {6268}, 2586: {1045}, 2587: {6274}, 2588: {6276}, 2589: {6279}, 2590: {6282}, 2591: {6287}, 2592: {6293}, 2593: {6303}, 2594: {6304}, 2595: {6306}, 2596: {6308}, 2597: {6318}, 2598: {6331}, 2599: {6333}, 2600: {6335}, 2601: {6336}, 2602: {6337}, 2603: {6348}, 2604: {6349}, 2605: {6353}, 2606: {6360}, 2607: {6362}, 2608: {6369}, 2609: {6370}, 2610: {6373}, 2611: {1063}, 2612: {6383}, 2613: {6384}, 2614: {1064}, 2615: {6388}, 2616: {6393}, 2617: {6394}, 2618: {6395}, 2619: {6404}, 2620: {6408}, 2621: {6413}, 2622: {6415}, 2623: {6422}, 2624: {6427}, 2625: {6429}, 2626: {6437}, 2627: {1073}, 2628: {6443}, 2629: {6453}, 2630: {6454}, 2631: {6455}, 2632: {6459}, 2633: {6460}, 2634: {6466}, 2635: {9270}, 2636: {6472}, 2637: {6478}, 2638: {6480}, 2639: {6481}, 2640: {6490}, 2641: {6491}, 2642: {6495}, 2643: {6506}, 2644: {6507}, 2645: {6513}, 2646: {6514}, 2647: {316}, 2648: {6518}, 2649: {6520}, 2650: {6521}, 2651: {6523}, 2652: {6528}, 2653: {490}, 2654: {6530}, 2655: {6531}, 2656: {6532}, 2657: {6536}, 2658: {6537}, 2659: {6539}, 2660: {6541}, 2661: {6552}, 2662: {6557}, 2663: {6558}, 2664: {6561}, 2665: {6563}, 2666: {6568}, 2667: {6569}, 2668: {6570}, 2669: {2461}, 2670: {6577}, 2671: {6579}, 2672: {6587}, 2673: {6588}, 2674: {6589}, 2675: {10656}, 2676: {6595}, 2677: {1100}, 2678: {6602}, 2679: {6604}, 2680: {6605}, 2681: {1101}, 2682: {6608}, 2683: {6612}, 2684: {6615}, 2685: {6617}, 2686: {6618}, 2687: {6624}, 2688: {6625}, 2689: {6627}, 2690: {6629}, 2691: {6646}, 2692: {2473}, 2693: {917}, 2694: {6657}, 2695: {6659}, 2696: {6661}, 2697: {6662}, 2698: {6669}, 2699: {6677}, 2700: {6683}, 2701: {6973}, 2702: {6690}, 2703: {6698}, 2704: {6701}, 2705: {6702}, 2706: {6703}, 2707: {6705}, 2708: {6706}, 2709: {10675}, 2710: {6712}, 2711: {6713}, 2712: {6716}, 2713: {10677}, 2714: {6724}, 2715: {6727}, 2716: {6731}, 2717: {6733}, 2718: {6734}, 2719: {6737}, 2720: {6740}, 2721: {6742}, 2722: {6744}, 2723: {6745}, 2724: {6750}, 2725: {9318}, 2726: {6769}, 2727: {6773}, 2728: {2495}, 2729: {6780}, 2730: {6781}, 2731: {6793}, 2732: {6797}, 2733: {1133}, 2734: {6818}, 2735: {6820}, 2736: {6826}, 2737: {6828}, 2738: {6829}, 2739: {6832}, 2740: {6837}, 2741: {6601}, 2742: {6843}, 2743: {9333}, 2744: {6849}, 2745: {10699}, 2746: {6853}, 2747: {6860}, 2748: {6868}, 2749: {6873}, 2750: {6874}, 2751: {6876}, 2752: {6878}, 2753: {8858}, 2754: {6887}, 2755: {6898}, 2756: {2515}, 2757: {6900}, 2758: {6903}, 2759: {6909}, 2760: {6912}, 2761: {6916}, 2762: {6919}, 2763: {6922}, 2764: {10711}, 2765: {6927}, 2766: {6929}, 2767: {6932}, 2768: {6934}, 2769: {1156}, 2770: {6939}, 2771: {10714}, 2772: {1868}, 2773: {6956}, 2774: {6965}, 2775: {1162}, 2776: {6974}, 2777: {6984}, 2778: {6988}, 2779: {9357}, 2780: {7002}, 2781: {7004}, 2782: {7014}, 2783: {7022}, 2784: {7036}, 2785: {1175}, 2786: {7061}, 2787: {7063}, 2788: {7071}, 2789: {1181}, 2790: {7091}, 2791: {7092}, 2792: {7097}, 2793: {7102}, 2794: {7104}, 2795: {2775}, 2796: {7119}, 2797: {7122}, 2798: {7126}, 2799: {7128}, 2800: {7129}, 2801: {10746}, 2802: {7136}, 2803: {7138}, 2804: {7152}, 2805: {7153}, 2806: {7154}, 2807: {1193}, 2808: {7169}, 2809: {7173}, 2810: {7175}, 2811: {7176}, 2812: {7177}, 2813: {7180}, 2814: {7190}, 2815: {7192}, 2816: {7198}, 2817: {9393}, 2818: {7212}, 2819: {7217}, 2820: {7218}, 2821: {7221}, 2822: {7230}, 2823: {7231}, 2824: {7235}, 2825: {7237}, 2826: {7240}, 2827: {7250}, 2828: {7251}, 2829: {7252}, 2830: {9401, 7557}, 2831: {7273}, 2832: {7274}, 2833: {1606}, 2834: {7277}, 2835: {7281}, 2836: {7285}, 2837: {7286}, 2838: {7289}, 2839: {7293}, 2840: {7295}, 2841: {7298}, 2842: {7302}, 2843: {7310}, 2844: {7311}, 2845: {7312}, 2846: {7322}, 2847: {7327}, 2848: {7329}, 2849: {7331}, 2850: {7342}, 2851: {7344}, 2852: {7347}, 2853: {7348}, 2854: {7349}, 2855: {7367}, 2856: {7368}, 2857: {7371}, 2858: {7380}, 2859: {7381}, 2860: {7383}, 2861: {7386}, 2862: {9423}, 2863: {7388}, 2864: {9677}, 2865: {7390}, 2866: {7399}, 2867: {519}, 2868: {7403}, 2869: {7404}, 2870: {7410}, 2871: {7418}, 2872: {7419}, 2873: {7427}, 2874: {7428}, 2875: {7431}, 2876: {7432}, 2877: {7436}, 2878: {7440}, 2879: {7448}, 2880: {7453}, 2881: {7457}, 2882: {7463}, 2883: {7465}, 2884: {7467}, 2885: {7468}, 2886: {7470}, 2887: {7475}, 2888: {7476}, 2889: {9438}, 2890: {7480}, 2891: {7481}, 2892: {7484}, 2893: {522}, 2894: {7491}, 2895: {7493}, 2896: {7502}, 2897: {7507}, 2898: {7508}, 2899: {2617}, 2900: {7524}, 2901: {7527}, 2902: {9447}, 2903: {7539}, 2904: {7545}, 2905: {7546}, 2906: {7549}, 2907: {7555}, 2908: {7560}, 2909: {9452}, 2910: {7562}, 2911: {7567}, 2912: {7576}, 2913: {7578}, 2914: {7584}, 2915: {7591}, 2916: {3051}, 2917: {1267}, 2918: {7607}, 2919: {7608}, 2920: {7617}, 2921: {7619}, 2922: {7620}, 2923: {9462}, 2924: {11165}, 2925: {7625}, 2926: {7628}, 2927: {10829}, 2928: {9465}, 2929: {10831}, 2930: {9466}, 2931: {7650}, 2932: {7653}, 2933: {7654}, 2934: {7658}, 2935: {7662}, 2936: {7666}, 2937: {7667}, 2938: {7668}, 2939: {7671}, 2940: {7672}, 2941: {7675}, 2942: {7677}, 2943: {7680}, 2944: {7689}, 2945: {7691}, 2946: {7700}, 2947: {7701}, 2948: {7707}, 2949: {7718}, 2950: {7722}, 2951: {2653}, 2952: {7733}, 2953: {7734}, 2954: {9481}, 2955: {7738}, 2956: {7743}, 2957: {7744}, 2958: {7745}, 2959: {7760}, 2960: {7766}, 2961: {7767}, 2962: {7771}, 2963: {6757}, 2964: {7776}, 2965: {7783}, 2966: {7790}, 2967: {7793}, 2968: {7795}, 2969: {7802}, 2970: {7811}, 2971: {7814}, 2972: {7815}, 2973: {7820}, 2974: {7821}, 2975: {7834}, 2976: {7837}, 2977: {7839}, 2978: {10864}, 2979: {7843}, 2980: {7845}, 2981: {7855}, 2982: {7856}, 2983: {7865}, 2984: {7880}, 2985: {7883}, 2986: {7892}, 2987: {6779}, 2988: {7910}, 2989: {7913}, 2990: {7915}, 2991: {7923}, 2992: {7929}, 2993: {7930}, 2994: {9514}, 2995: {7937}, 2996: {7938}, 2997: {7943}, 2998: {7946}, 2999: {7947}, 3000: {7951}, 3001: {7955}, 3002: {7957}, 3003: {7959}, 3004: {7960}, 3005: {7962}, 3006: {7963}, 3007: {7975}, 3008: {7977}, 3009: {10887}, 3010: {7985}, 3011: {7988}, 3012: {7990}, 3013: {7991}, 3014: {7992}, 3015: {2994}, 3016: {8001}, 3017: {8003}, 3018: {8162}, 3019: {8016}, 3020: {2703}, 3021: {8028}, 3022: {8033}, 3023: {8039}, 3024: {8042}, 3025: {8047}, 3026: {268}, 3027: {8058}, 3028: {8067}, 3029: {8070}, 3030: {8078}, 3031: {10904}, 3032: {8082}, 3033: {9540}, 3034: {8092}, 3035: {8093}, 3036: {8104}, 3037: {8115}, 3038: {8123}, 3039: {8126}, 3040: {8132}, 3041: {8135}, 3042: {8138}, 3043: {2722}, 3044: {8142}, 3045: {8144}, 3046: {8155}, 3047: {8161}, 3048: {9819}, 3049: {8165}, 3050: {8166}, 3051: {8189}, 3052: {8181}, 3053: {10922}}
In [7]:
len(clus)
Out[7]:
4126
In [8]:
sizes = list(map(len, clus.values()))
np.sort(sizes)[-100:]
Out[8]:
array([ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 7,
8, 9, 10, 12, 13, 15, 22, 46, 117, 283, 334,
6077])
In [9]:
max_c = max(clus, key=lambda c: len(clus[c]))
print(max_c)
3
In [10]:
[c for c in clus if len(clus[c]) == 2]
Out[10]:
[67,
139,
221,
303,
311,
320,
337,
346,
367,
377,
444,
493,
548,
629,
631,
678,
709,
713,
765,
779,
783,
796,
855,
868,
900,
987,
994,
1122,
1150,
1177,
1259,
1260,
1272,
1315,
1386,
1388,
1450,
1469,
1506,
1520,
1614,
1685,
1688,
1766,
1787,
1852,
1911,
1961,
2005,
2019,
2057,
2105,
2163,
2164,
2166,
2207,
2228,
2242,
2343,
2377,
2419,
2467,
2513,
2583,
2617,
2618,
2704,
2753,
2919,
2966,
2994,
3052,
3183,
3290,
3389,
3444,
3461,
3513,
3902]
In [11]:
def sign_percent(g):
df = pd.DataFrame([(i, j, g[i][j]['sign'])
for i, j in g.edges_iter()], columns=('u', 'v', 's'))
return df['s'].value_counts() / df.shape[0]
In [12]:
main_component = g.subgraph(clus[max_c])
sign_percent(main_component)
Out[12]:
1.0 0.871442
-1.0 0.128558
Name: s, dtype: float64
In [13]:
sign_percent(g)
Out[13]:
1.0 0.774805
-1.0 0.225195
Name: s, dtype: float64
In [ ]:
g[227]
In [ ]:
g.number_of_nodes()
In [ ]:
for l, nodes in clus.items():
for n in nodes:
g.node[n]['ClusterId'] = l
In [ ]:
nx.write_gpickle(g, 'output/clus_wiki.pkl')
In [ ]:
g = nx.read_gpickle('output/clus_wiki.pkl')
In [ ]:
g.node[0]
In [ ]:
for n in g.nodes_iter():
del g.node[n]['cc_label']
In [ ]:
nx.write_gml(g, 'output/clus_wiki.gml')
Content source: xiaohan2012/snpp
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